{ "fetched_at": "2026-10-25T08:35:03Z", "source_url": "https://www.kaggle.com/competitions/2025-ieee-icip-vip-cup/data", "pages": [ { "name": "data-description", "content": "# Dataset Description\n\nThis section describes exactly the datasets provided for the competition, based solely on the official document. It covers dataset splits, modalities, counts, and organization as specified in Section 2.2 and Section 2.5 of the Official Document .\n\n---\n\n## 1. Overview\n\nThe competition provides two primary datasets, as detailed in Section 2.2:\n\n1. **Drone Detection and Tracking Dataset** \n - **Type(s)**: IR/Thermal, RGB/Visual \n - **Modality**: Images and Videos \n - **Number of classes**: 2 (Drone and Bird) \n - **Train set**: 45,000 IR–RGB image pairs \n - **Validation set**: 6,500 IR and RGB images + 40 video sequences \n - **Test set**: 13,000 IR and RGB images + 25 video sequences \n - **Frame rate of videos**: 30 fps \n - **Resolution of each video**: 320 × 256 pixels \n - **Image size**: 320 × 256 pixels \n - **Duration of each video**: 10 s \n - **Environmental factors considered**: Hilly regions, thick forest cover, cloudy sky, fog and mist \n - **Scenarios**: Single class in a frame, multiple classes in a frame, objects far off from the FoV of the source camera, swarm of birds and drones, drones captured under extreme environmental conditions as stated above \n - **Distortions considered**: Speckle noise, salt and pepper noise, Gaussian blur, uneven illumination, motion blur, camera instability and AWGN \n - **Number of videos with distortions**: 30 \n - **Intensity of distortions (on a scale of 5)**:\n - Speckle noise – 2 \n - Salt and Pepper noise – 2 \n - Gaussian blur – 4 \n - Uneven illumination – 4 \n - Motion blur – 4 \n - Camera instability – 5 \n - AWGN – 3 \n :contentReference[oaicite:5]{index=5}\n\n2. **Payload Identification Dataset** \n - **Type(s)**: IR/Thermal, RGB/Visual \n - **Modality**: Images \n - **Number of classes**: 2 (Harmful payload and Normal payload) \n - **Train set**: 25,000 IR–RGB image pairs \n - **Validation set**: 4,000 IR and RGB images \n - **Test set**: 8,000 IR and RGB images \n - **Image size**: 320 × 256 pixels \n - **Environmental factors considered**: Hilly regions, thick forest cover, cloudy sky, fog and mist \n - **Scenarios**: Single class in a frame, multiple classes in a frame, objects far off from the FoV of the source camera, swarm of birds and drones, drones captured under extreme environmental conditions as stated above \n - **Distortions considered**: Speckle noise, salt and pepper noise, Gaussian blur, uneven illumination, motion blur, camera instability and AWGN \n - **Intensity of distortions (on a scale of 5)**:\n - Speckle noise – 2 \n - Salt and Pepper noise – 2 \n - Gaussian blur – 4 \n - Uneven illumination – 4 \n - Motion blur – 4 \n - Camera instability – 5 \n - AWGN – 3 \n :contentReference[oaicite:6]{index=6}\n\n*(All values above are taken verbatim from Section 2.2 “Dataset Description” of the Official Document.)*\n\n---\n\n## 2. Data Organization\n\nAs specified in Section 2.5 of the Official Document, the directory layout and file organization are as follows:\n\n- **Main Directory**: `Data/` \n - **RGB/** \n - **Train/**: Folder containing `Images/` and `Labels/` \n - **Validation/**: Folder containing `Images/` and `Labels/` \n - **Test/**: Folder containing `Images/` and `Labels/` \n - **IR/** \n - **Train/**: Folder containing `Images/` and `Labels/` \n - **Validation/**: Folder containing `Images/` and `Labels/` \n - **Test/**: Folder containing `Images/` and `Labels/` \n - **Payload/** \n - **Train/**: Folder containing `Images/` and `Labels/` \n - **Validation/**: Folder containing `Images/` and `Labels/` \n - **Test/**: Folder containing `Images/` and `Labels/` \n- **Additional files in the root**:\n - `Dataset_Statistics.txt` – File describes dataset statistics \n - `Challenge_Description.txt` – File describes the challenge \n:contentReference[oaicite:7]{index=7}\n\n> **Note**: The actual filenames within `Images/` and `Labels/` folders (e.g., exact image file naming conventions, label file formats) are provided in these subfolders; this section reflects only the high-level folder structure as given.\n\n---\n\n## 3. How to Present in Kaggle “Data” Tab\n\nCopy the above sections under your Kaggle competition Data page or README.md:\n\n1. **Header**: `# Dataset Description`\n2. **Subsections**: \n - `## 1. Overview` containing the two dataset descriptions (Drone Detection and Tracking Dataset; Payload Identification Dataset) with exact parameters. \n - `## 2. Data Organization` with the directory layout as above.\n\nNo other details or placeholder values are included—only what the official document specifies.\n\n---\n\n## Example Kaggle Markdown Block\n\n```markdown\n# Dataset Description\n\nThis section describes exactly the datasets provided for the competition, based solely on the official document.\n\n---\n\n## 1. Overview\n\nThe competition provides two primary datasets:\n\n1. **Drone Detection and Tracking Dataset** \n - **Type(s)**: IR/Thermal, RGB/Visual \n - **Modality**: Images and Videos \n - **Number of classes**: 2 (Drone and Bird) \n - **Train set**: 45,000 IR–RGB image pairs \n - **Validation set**: 6,500 IR and RGB images + 40 video sequences \n - **Test set**: 13,000 IR and RGB images + 25 video sequences \n - **Frame rate of videos**: 30 fps \n - **Resolution of each video**: 320 × 256 pixels \n - **Image size**: 320 × 256 pixels \n - **Duration of each video**: 10 s \n - **Environmental factors considered**: Hilly regions, thick forest cover, cloudy sky, fog and mist \n - **Scenarios**: Single class in a frame, multiple classes in a frame, objects far off from the FoV of the source camera, swarm of birds and drones, drones captured under extreme environmental conditions as stated above \n - **Distortions considered**: Speckle noise, salt and pepper noise, Gaussian blur, uneven illumination, motion blur, camera instability and AWGN \n - **Number of videos with distortions**: 30 \n - **Intensity of distortions (on a scale of 5)**:\n - Speckle noise – 2 \n - Salt and Pepper noise – 2 \n - Gaussian blur – 4 \n - Uneven illumination – 4 \n - Motion blur – 4 \n - Camera instability – 5 \n - AWGN – 3 \n\n2. **Payload Identification Dataset** \n - **Type(s)**: IR/Thermal, RGB/Visual \n - **Modality**: Images \n - **Number of classes**: 2 (Harmful payload and Normal payload) \n - **Train set**: 25,000 IR–RGB image pairs \n - **Validation set**: 4,000 IR and RGB images \n - **Test set**: 8,000 IR and RGB images \n - **Image size**: 320 × 256 pixels \n - **Environmental factors considered**: Hilly regions, thick forest cover, cloudy sky, fog and mist \n - **Scenarios**: Single class in a frame, multiple classes in a frame, objects far off from the FoV of the source camera, swarm of birds and drones, drones captured under extreme environmental conditions as stated above \n - **Distortions considered**: Speckle noise, salt and pepper noise, Gaussian blur, uneven illumination, motion blur, camera instability and AWGN \n - **Intensity of distortions (on a scale of 5)**:\n - Speckle noise – 2 \n - Salt and Pepper noise – 2 \n - Gaussian blur – 4 \n - Uneven illumination – 4 \n - Motion blur – 4 \n - Camera instability – 5 \n - AWGN – 3 \n\n*(All values above are taken verbatim from Section 2.2 of the Official Document.)*\n\n---\n\n## 2. Data Organization\n\nAs specified in Section 2.5 of the Official Document:\n\n- **Main Directory**: `Data/` \n - **RGB/** \n - **Train/**: Folder containing `Images/` and `Labels/` \n - **Validation/**: Folder containing `Images/` and `Labels/` \n - **Test/**: Folder containing `Images/` and `Labels/` \n - **IR/** \n - **Train/**: Folder containing `Images/` and `Labels/` \n - **Validation/**: Folder containing `Images/` and `Labels/` \n - **Test/**: Folder containing `Images/` and `Labels/` \n - **Payload/** \n - **Train/**: Folder containing `Images/` and `Labels/` \n - **Validation/**: Folder containing `Images/` and `Labels/` \n - **Test/**: Folder containing `Images/` and `Labels/` \n- **Additional files** in root:\n - `Dataset_Statistics.txt` – File describes dataset statistics \n - `Challenge_Description.txt` – File describes the challenge \n\n*(Folder and file names are exactly as provided in the Official Document.)*\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "Team Composition and Eligibility\n\nEach team MUST be composed of: (i) One faculty member (the Supervisor) and (ii) At least 3 but no more than 10 undergraduates; Optionally (iii) At most one graduate student (the Tutor).\nAt least three of the undergraduate team members must be SPS student members at time of team registration.\n\nFurther definitions of each team position are as follows:\nFaculty (Supervisor): Postdocs and research associates are not considered as faculty members.\nGraduate Student (Tutor): A graduate student is a student having earned at least a 4-year University degree at the time of submission. Please note: Tutors are not eligible to receive travel grants or prize money.\nUndergraduate: An undergraduate student is a student without a 4-year degree.\nTeam members cannot be changed after the team registration deadline.\n\nAt least one undergraduate team member must be in attendance (in-person) of the final round of the competition held at the respective conference (ICIP) to present the team’s work.*", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "Please refer to the official VIP Cup document for complete details about evaluation criteria and submission requirements. \n\nhttps://2025.ieeeicip.org/wp-content/uploads/sites/679/2025-VIP-Cup-Competition-Official-Document_Version-1.1-with-reg-link.pdf", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "# Competition Overview\n\n“2025 Video and Image Processing Cup \nInfrared-Visual Fusion for Enhanced Drone Detection, Tracking and Payload Identification in Surveillance Videos” :contentReference[oaicite:19]{index=19}\n\nUnmanned Aerial Vehicles (UAVs), commonly referred to as drones, have gained significant prominence in recent years due to their diverse applications in areas such as surveillance, delivery, agriculture, logistics, disaster response, and military operations. However, the proliferation of drones has also introduced critical challenges such as unauthorized aerial activity, potential threats from payload delivery systems including payload delivery for malicious purposes as well as major security and privacy concerns. This necessitates the detection and tracking of drones, along with the identification of their payloads to ensure safety and security, particularly in sensitive or restricted areas. \n\nTraditional vision-based approaches for drone detection predominantly rely on RGB images, which are often hindered by environmental factors such as low light, fog, or glare. On the other hand, infrared (IR) imaging provides complementary information by capturing thermal signatures enabling robust detection in challenging conditions such as nighttime or occlusion even in cases where the drones are exceedingly far from the field of view (FoV) of the surveillance cameras. Although it may seem that infrared imaging outperforms RGB imaging in such cases, standalone use of IR data may lack the spatial and textural richness provided by RGB images. This makes it essential to combine the two modalities to leverage their complementary strengths. \n\nAlongside drone detection, the ability to detect and identify payloads carried by drones is critical due to the potential risks associated with unauthorized or malicious payload delivery. Payloads may include hazardous materials, surveillance equipment, or contraband, posing threats to public safety, security, and privacy. Identifying payloads in real-time enables proactive measures to mitigate risks making it an essential aspect of drone monitoring systems. However, identifying and analyzing payloads carried by drones poses unique challenges. Payloads may vary in size, shape and thermal properties making it difficult to rely on a single modality for robust detection. RGB imaging provides critical visual cues for recognizing payload shapes and textures while IR imaging can highlight heat signatures of payloads, particularly those carrying heat-generating devices or components. A fusion-based approach is therefore crucial to enhance payload detection accuracy and reliability. :contentReference[oaicite:20]{index=20}\n\n## Timeline\n\n6. Important Dates \n• Challenge announcement 30 March 2025 \n• Availability of Training Data – 10th April 2025 \n• Team Registration Deadline: 30 April 2025 \n• Availability of Test Data – 20th May 2025 \n• Final submission due: 15 June 2025 \n• Finalists announcement: 15 July 2025 \n• Presentation by top 3 teams and announcement of winners at ICIP 2025: September 14-17, 2025 \n\n*(All deadlines follow America/New_York local time as implied by the document context.)*\n\n---\n\n# Data Description\n\n## 2.2 Dataset Description\n\n**Drone Detection and Tracking Dataset:** \nParameter Considered Description \nType(s) IR/Thermal, RGB/Visual \nModality of the dataset Images and Videos \nNumber of classes 2 (Drone and Bird) \nNumber of images and videos in the train set 45000 IR-RGB image pairs \nNumber of images and videos in the validation set 6500 IR and RGB images + 40 video sequences \nNumber of images and videos in the test set 13000 IR and RGB images + 25 video sequences \nFrame rate of videos in the dataset 30 fps \nResolution of each video 320 x 256 pixels \nImage size 320 x 256 \nDuration of each video 10 s \nEnvironmental factors considered Hilly regions, thick forest cover, cloudy sky, fog and mist \nScenarios Single class in a frame, multiple classes in a frame, objects far off from the FoV of the source camera, swarm of birds and drones, drones captured under extreme environmental conditions as stated above \nDistortions considered Speckle noise, salt and pepper noise, Gaussian blur, uneven illumination, motion bur, camera instability and AWGN \nNumber of videos with distortions 30 \nIntensity of distortions (on a scale of 5) \n- Speckle noise – 2 \n- Salt and Pepper noise – 2 \n- Gaussian blur – 4 \n- Uneven illumination – 4 \n- Motion blur – 4 \n- Camera instability – 5 \n- AWGN – 3 :contentReference[oaicite:21]{index=21}\n\n**Payload Identification Dataset:** \nParameter Considered Description \nType(s) IR/Thermal, RGB/Visual \nModality of the dataset Images \nNumber of classes 2 (Harmful payload and Normal payload) \nNumber of images in the train set 25000 IR-RGB image pairs \nNumber of images in the validation set 4000 IR and RGB images \nNumber of images in the test set 8000 IR and RGB images \nImage size 320 x 256 \nEnvironmental factors considered Hilly regions, thick forest cover, cloudy sky, fog and mist \nScenarios Single class in a frame, multiple classes in a frame, objects far off from the FoV of the source camera, swarm of birds and drones, drones captured under extreme environmental conditions as stated above \nDistortions considered Speckle noise, salt and pepper noise, Gaussian blur, uneven illumination, motion bur, camera instability and AWGN \nIntensity of distortions (on a scale of 5) \n- Speckle noise – 2 \n- Salt and Pepper noise – 2 \n- Gaussian blur – 4 \n- Uneven illumination – 4 \n- Motion blur – 4 \n- Camera instability – 5 \n- AWGN – 3 :contentReference[oaicite:22]{index=22}\n\n## 2.5 Data organization\n\nThe data is organized as follows: \nMain Directory Sub Directory Dataset Split Type Contents \nData/ \n RGB/ \n Train/ Folder Images/ , Labels/ \n Validation/ Folder Images/ , Labels/ \n Test/ Folder Images/ , Labels/ \n IR/ \n Train/ Folder Images/ , Labels/ \n Validation/ Folder Images/ , Labels/ \n Test/ Folder Images/ , Labels/ \n Payload/ \n Train/ Folder Images/ , Labels/ \n Validation/ Folder Images/ , Labels/ \n Test/ Folder Images/ , Labels/ \nDataset_Statistics.txt - File Describes dataset statistics \nChallenge_Description.txt - File Describes the challenge :contentReference[oaicite:23]{index=23}\n\n*(Use exactly this folder structure when packaging data on Kaggle Datasets.)*\n\n---\n\n# Evaluation Metrics\n\n## 3. Evaluation Metrics (from Section 3 and 2.4 Evaluation Protocols)\n\n### Task 1: RGB + IR Drone Detection\n\nFinal Composite Score Formula: \n· S_det = ((A + F₁ + P + R) / 4) × R_b × S_d \nWhere: \n· • A = Accuracy (Normalized as a decimal, e.g., 92% → 0.92) \n· • F₁ = F1 Score (Normalized similarly to Accuracy) \n· • P = Precision (Normalized as a decimal) \n· • R = Recall (Normalized as a decimal) \n· • R_b = Normalized Robustness Score \n - Calculated as the average ratio of the performance metric (F1 Score) achieved under each adverse condition to a benchmark maximum for that condition. \n - Example: R_b = (1/N) * Σ (F1_i / F1_max_i), for N adverse conditions. \n· • S_d = Normalized Inference Speed \n - Calculated as the ratio of the model's achieved FPS to a target FPS (e.g., 30 FPS), capped at 1. \n - Formula: S_d = min(1, FPS_model / FPS_target). :contentReference[oaicite:24]{index=24}\n\n### Task 2: RGB + IR Drone Tracking\n\nFinal Composite Score Formula: \n· S_track = ((I + C + D) / 3) × S_t \nWhere: \n· • I = Average Intersection-over-Union (IoU) between predicted and ground truth trajectories \n· • C = Tracking Consistency factor \n - C = 1 - (M / 15), where M is the number of consecutive missed frames (for M < 15; if M ≥ 15, C = 0) \n· • D = Directional Accuracy \n - Indicates the accuracy in determining whether the drone is approaching or receding from the camera’s field of view. \n· • S_t = Normalized Inference Speed for Tracking \n - Computed similarly to S_d, using the FPS achieved during tracking relative to a target FPS. :contentReference[oaicite:25]{index=25}\n\n### Task 3: Payload Detection\n\nFinal Composite Score Formula: \n· S_payload = (C_score + mAP) / 2 \nWhere: \n· • C_score = (A + F₁ + P + R) / 4 \n - A, F₁, P, and R are the standard classification metrics for detecting payload types, each normalized to [0, 1]. \n· • mAP = Mean Average Precision \n - Assesses the overall detection quality, especially in scenarios with multiple payload instances, normalized to [0, 1]. :contentReference[oaicite:26]{index=26}\n\n### Overall Team Ranking\n\nFinal Overall Ranking Formula: \n· S_overall = w₁ * S_det + w₂ * S_track + w₃ * S_payload \nFor equal weighting (w₁ = w₂ = w₃ = 1/3): \n· S_overall = (S_det + S_track + S_payload) / 3 :contentReference[oaicite:27]{index=27}\n\n*(This ensures performance across detection, tracking, and payload identification is integrated exactly as in the Official Document.)*\n\n---\n\n# Competition Rules\n\n## 4. Participants submissions (Section 4)\n\n4.1 Results \nSubmission Guidelines: \nEach participating team must submit their complete solution in a zipped file via the challenge webpage. The submission must adhere to the following specifications: \n● File Naming Format: \n Name your zipped file as: TeamName_IR_RGB_Drone_ICIP2025 \n● Contents of the Zipped File: \n 1. Main Code - containing the complete implementation details with comments. \n 2. Demo Code: a separate or integrated demo code that can be executed on a test video to evaluate the submitted solution. \nNote: Incomplete submissions that do not include scores/results for all trials or required components will be disqualified. :contentReference[oaicite:28]{index=28}\n\n4.2 Technical Report \nEach team must include a short technical report in their zipped submission. This report should provide a clear summary of your approach and must cover the following areas: \n● System Architecture: \n ○ Describe your fusion-based approach that integrates RGB and infrared (IR) data for enhanced drone detection, tracking, and payload identification. \n ○ Explain the data preprocessing steps, sensor fusion techniques, and overall algorithmic pipeline. \n● Algorithmic Implementation: \n ○ Provide a detailed explanation of the algorithms used for: \n ■ Detecting drones and differentiating them from birds in images/videos. \n ■ Tracking drones, including methods for computing the trajectory and determining whether a drone is approaching or receding from the FoV of the camera source. \n ■ Detecting the type of distortions (e.g., low lighting, fog, motion blur) and handling these challenges. \n ○ Justify your design choices and any optimization techniques employed. \n● Demo Code Description: \n ○ Explain how the demo code functions, highlighting how detection confidence scores, distortion types, bounding box outputs, and drone trajectories are visualized. Describe how the demo code facilitates real-time evaluation of your solution. \n● Performance Metrics: \n ○ Summarize the performance of your system using the evaluation metrics defined in Section 3 (e.g., accuracy, IoU, mAP, inference speed). \n● System Specifications: \n ○ Detail the hardware and software environment used, including inference times on both CPU and GPU. \nSubmission Deadline: \n Ensure that your complete submission (zipped file with all required items) is received by the final submission deadline. :contentReference[oaicite:29]{index=29}\n\n## 5. SPS Competition Terms & Conditions (Section 5.1)\n\n5.1.a. Team Composition \n⮚ Each team MUST be composed of: (i) One faculty member (the Supervisor) and (ii) At least 3 but no more than 10 undergraduates; Optionally (iii) At most one graduate student (the Tutor). \no At least three of the undergraduate team members must be SPS student members at time of team registration, and maintain this status until the end of the competition \no Faculty (Supervisor): Postdocs and research associates are not considered as faculty members. \no Graduate Student (Tutor): A graduate student is a student having earned at least a 4-year University degree at the time of submission. Please note: Tutors are not eligible to receive travel grants or prize money. \no Undergraduate: An undergraduate student is a student without a 4-year degree. \n⮚ Team members cannot be changed after the team registration deadline. \n⮚ At least one undergraduate team member must be in attendance (in-person) of the final round of the competition held at the respective conference (ICASSP or ICIP) to present the team’s work.* \n*Important notice: Upon registering a team for the competition, the team must commit to at least one undergraduate team member representing the team by attending the physical competition and participating in the final round of the competition at the physical conference. Should a team not be able to participate physically (in-person) in the final round of the competition held at the respective conference (ICASSP or ICIP) for any reason, at any point in the competition, then the team must notify SPS Staff and organizers immediately. This will result in the team being ineligible to continue in the competition, therefore forfeiting their position in the competition. Teams must make every effort to attend the final round at the conference; visa issues may be an exception. If all team members are unable to obtain visas, please be prepared to present proof of visa process, communication to obtain visa, as well as a visa denial. All eligibility decisions are at the discretion of the SPS Student Services Committee and competition organizers. Should a team be disqualified or forfeit their finalist position for any reason, the next team selected by the organizers may be contacted to compete in the final round, following the same guidelines as above. :contentReference[oaicite:30]{index=30}\n\n5.1.b Team ineligibility (Further clarification) \nSpecific team ineligibility in addition to the above. Any of these criteria will result in the team being disqualified/ineligible to continue in the competition: \n⮚ Teams that are composed with 50% or more of its undergraduate or tutor members being students who have previously participated on a finalist team within the last year are not eligible. For example, if two of four undergraduate team members participated as a member of a finalist team during the 2023 SP Cup will not be eligible to participate on a team for the 2023 VIP Cup. \n⮚ Any team members who have placed in the top three teams of any SPS competition held during the previous conference, i.e. a member from one of the 3 finalist teams of the 2023 SP Cup (at ICASSP) will not be eligible to participate in the 2023 VIP Cup (at ICIP). Any team member that has received SPS travel support within the previous year would not be eligible to receive travel support again if their team is selected as a finalist. :contentReference[oaicite:31]{index=31}\n\n## 6. Prizes for Finalists (Section 5.4)\n\nThe [eligible] three teams with the highest performance in the open competition based on the judging criteria will be selected as finalists and invited to participate in the final competition at ICASSP or ICIP. The champion team will receive a Grand Prize of $5,000. The first and the second runner-up teams will receive a prize of $2,500 and $1,500, respectively, in addition to travel grants and complimentary conference registrations for up to three team members. \n⮚ Up to three undergraduate student members from each finalist team will be provided travel support to attend the conference in-person. In-person attendance of the physical conference is required for reimbursement. \n o Travel grant funds are offered on a reimbursement basis of up to $1,200 for continental travel and $1,700 for intercontinental travel. There are no exceptions. \n o Funds will be issued by way of a bank transfer after the competition via Concur. \n o Receipts for reimbursement must be uploaded into Concur \n⮚ Complimentary conference registration for up to three undergraduate team members. \n o These complimentary conference registrations cannot be used to cover any papers accepted by the conference. \n o You must notify Jaqueline Rash, Jaqueline.rash@ieee.org, via email of the three team members who have been chosen to receive complimentary registration. \n o VISA information: Once registered for the conference, each individual will have the opportunity to request an invitation letter through the conference website to be used for the visa application process. This is the only way to receive a letter for visa purposes. \n⮚ The finalist teams will also be invited to join the Conference Banquet and the SPS Student Job Fair, so that they can meet and talk to SPS leaders and global experts. Please note registration for the Conference Banquet and Student Job Fair is limited and based on availability. You must add this event to your registration. If you are unable to add these events, then maximum capacity has been reached. There may be additional availability for the Student Job Fair, but not the Conference Banquet. You can inquire by emailing Jaqueline.Rash@ieee.org. :contentReference[oaicite:32]{index=32}\n\n## 7. Registration and Important Resources (Section 7)\n\n7.1 Official SP Cup Team Registration \nAll teams must be registered through the official competition registration system in order to be considered as a participating team. Official team registration must be submitted via: \nhttps://www2.securecms.com/VIPCup/VIPRegistration.asp \nAll teams MUST acknowledge and agree to the ‘SP and VIP Cup Terms & Conditions’ to be eligible for the competition. :contentReference[oaicite:33]{index=33}\n\n---\n\n# Contacts\n\nCompetition Organizers (technical, competition-specific inquiries): \nVishnu Ravishankar \nEmail: vishnuravishankar27@gmail.com \nDharini Raghavan \nEmail: dhariniraghavan2001@gmail.com \nDr S Sethu Selvi \nEmail: selvi@msrit.edu \nDr Raghuram S \nEmail: raghuram@msrit.edu \nDr Sitaram Ramachandrula \nEmail: NagaVenkata.R@247.ai \nShefali Singh \nEmail: singhshefali921@gmail.com \nSangeetha Kar \nEmail: sangeeta.you@gmail.com \nSuman Jangid \nEmail: sumanjangid1782@gmail.com \nSPS Staff (Terms & Conditions, Travel Grants, Prizes): \nJaqueline Rash, SPS Membership Program and Events Manager \nEmail: Jaqueline.Rash@ieee.org \nSPS Student Services Committee \nLucas Thomaz, Chair \nEmail: lucas.thomaz@smt.ufrj.br :contentReference[oaicite:34]{index=34}\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have gained significant prominence in recent years due to their diverse applications in areas such as surveillance, delivery, agriculture, logistics, disaster response, and military operations. However, the proliferation of drones has also introduced critical challenges such as unauthorized aerial activity, potential threats from payload delivery systems including payload delivery for malicious purposes as well as major security and privacy concerns. This necessitates the detection and tracking of drones, along with the identification of their payloads to ensure safety and security, particularly in sensitive or restricted areas.\n\nTraditional vision-based approaches for drone detection predominantly rely on RGB images, which are often hindered by environmental factors such as low light, fog, or glare. On the other hand, infrared (IR) imaging provides complementary information by capturing thermal signatures enabling robust detection in challenging conditions such as nighttime or occlusion even in cases where the drones are exceedingly far from the field of view (FoV) of the surveillance cameras. Although it may seem that infrared imaging outperforms RGB imaging in such cases, standalone use of IR data may lack the spatial and textural richness provided by RGB images. This makes it essential to combine the two modalities to leverage their complementary strengths.\n\nAlongside drone detection, the ability to detect and identify payloads carried by drones is critical due to the potential risks associated with unauthorized or malicious payload delivery. Payloads may include hazardous materials, surveillance equipment, or contraband, posing threats to public safety, security, and privacy. Identifying payloads in real-time enables proactive measures to mitigate risks making it an essential aspect of drone monitoring systems. However, identifying and analyzing payloads carried by drones poses unique challenges. Payloads may vary in size, shape and thermal properties making it difficult to rely on a single modality for robust detection. RGB imaging provides critical visual cues for recognizing payload shapes and textures while IR imaging can highlight heat signatures of payloads, particularly those carrying heat-generating devices or components. A fusion-based approach is therefore crucial to enhance payload detection accuracy and reliability.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "Total Prizes Available: 3\n- **1st Place** - $5000\n- **2nd Place** - $2500\n- **3rd Place** - $1500\n\nAll participating teams will receive a certificate of participation.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "foundational-rules", "content": "The following Kaggle Competition Foundational Rules (“ Foundational Rules ”) apply to every competition regardless of whether the Sponsor creates competition-specific rules. Any competition-specific rules provided by the Sponsor are in addition to these rules, and in the case of any conflict or inconsistency, these Foundational Rules control and nullify contrary competition-specific rules.\n###GENERAL COMPETITION RULES - BINDING AGREEMENT\n####1. ELIGIBILITY\na. To be eligible to enter the Competition, you must be:\n1. a registered account holder at Kaggle.com; \n2. the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); \n3. not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, or North Korea; and\n4. not a person or representative of an entity under U.S. export controls or sanctions (see: [https://www.treasury.gov/resourcecenter/sanctions/Programs/Pages/Programs.aspx][1]).\n\nb. Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Host reserves the right to forego or award alternative Prizes where needed to comply with local laws. If a winner is located in a country where prizes cannot be awarded, then they are not eligible to receive a prize.\n\nc. If you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or where you are an employee. If you are acting within the scope of your employment, or as an agent of another party, you warrant that such party or your employer has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures. \n\nd. The Competition Sponsor reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.\n\n####2. SPONSOR AND HOSTING PLATFORM\n\na. The Competition is sponsored by Competition Sponsor named above. The Competition is hosted on behalf of Competition Sponsor by Kaggle Inc. (\"Kaggle\"). Kaggle is an independent contractor of Competition Sponsor, and is not a party to this or any agreement between you and Competition Sponsor. You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at [www.kaggle.com/terms][2] in addition to these Rules.\n\n####3. COMPETITION PERIOD\na. For the purposes of Prizes, the Competition will run from the Start Date and time to the Final Submission Deadline (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Sponsor may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.\n\n####4. COMPETITION ENTRY\na. NO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline, and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.\nb. Submissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\nc. If the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.\nd. Submissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Sponsor reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements. \n\n####5. INDIVIDUALS AND TEAMS\na. Individual Account. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account, or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website. \nb. Teams. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a Team; however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.\nc. Team Merger. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the earlier of: any merger deadline or the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules. \nd. Private Sharing. No private sharing outside of Teams. Privately sharing code or data outside of Teams is not permitted. It's okay to share code if made available to all Participants on the forums.\n\n####6. SUBMISSION CODE REQUIREMENTS\na. Private Code Sharing. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\nb. Public Code Sharing. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open Source Initiative-approved license (see [www.opensource.org][3]) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.\nc. Use of Open Source. Unless otherwise stated in the Specific Competition Rules above, if open source code is used in the model to generate the Submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see [www.opensource.org][4]) that in no event limits commercial use of such code or model containing or depending on such code.\n\n####7. DETERMINING WINNERS\na. Each Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's Public Leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the Private Leaderboard, subject to compliance with these Rules. The Public Leaderboard will be based on the public test set and the Private Leaderboard will be based on the private test set.\nb. In the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.\n\n####8. NOTIFICATION OF WINNERS & DISQUALIFICATION\na. The potential winner(s) will be notified by email. \nb. If a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the Final Submission Deadline that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.\nc. In case (i) and (ii) above Kaggle may disqualify the Participant. However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the Participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the Participant will not be disqualified pursuant to this paragraph.\nd. Competition Sponsor reserves the right to disqualify any Participant from the Competition if the Competition Sponsor reasonably believes that the Participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other Participants, Competition Sponsor or Kaggle.\ne. A disqualified Participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.\nf. The final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.\n\n####9. PRIZES\na. Prize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odds of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the Participants. \nb. All Prizes are subject to Competition Sponsor's review and verification of the Participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Sponsor may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).\nc. A potential winner may decline to be nominated as a Competition winner in accordance with Section 3.8.\nd. Potential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately thirty (30) days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. \ne. You are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2.7 and Section 3.1 above.\nf. If a Team wins a monetary Prize, the Prize money will be allocated in even shares between the eligible Team members, unless the Team unanimously opts for a different Prize split and notifies Kaggle before Prizes are issued.\n\n####10. TAXES\na. ALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS. Payments to potential winners are subject to the express requirement that they submit all documentation requested by Competition Sponsor or Kaggle for compliance with applicable state, federal, local and foreign (including provincial) tax reporting and withholding requirements. Prizes will be net of any taxes that Competition Sponsor is required by law to withhold. If a potential winner fails to provide any required documentation or comply with applicable laws, the Prize may be forfeited and Competition Sponsor may select an alternative potential winner. Any winners who are U.S. residents will receive an IRS Form-1099 in the amount of their Prize.\n\n####11. GENERAL CONDITIONS\na. All federal, state, provincial and local laws and regulations apply.\n\n####12. PUBLICITY\na. You agree that Competition Sponsor, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.\n\n####13. PRIVACY\na. You acknowledge and agree that Competition Sponsor and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy <[www.kaggle.com/privacy][6]>, including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at <[www.kaggle.com/contact][7]>.\nb. As part of Competition Sponsor performing this contract between you and the Competition Sponsor, Kaggle will transfer your Personal Information to Competition Sponsor, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Sponsor agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Sponsor in the country specified in the Competition Sponsor Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.\n\n####14. WARRANTY, INDEMNITY AND RELEASE \na. You warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses. You agree not to make any Submission that: (i) infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.\nb. To the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorneys fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.\nc. You hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.\n\n####15. INTERNET\na. Competition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a Participant’s ability to participate.\n\n####16. RIGHT TO CANCEL, MODIFY OR DISQUALIFY\na. If for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any Participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a Participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such Participant to the fullest extent of the applicable law.\n\n####17. NOT AN OFFER OR CONTRACT OF EMPLOYMENT\na. Under no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.\n\n####18. DEFINITIONS\na. \"Competition Data\" are the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to Participants. \nb. An “Entry” is when a Participant has joined, signed up, or accepted the rules of a competition. Entry is required to make a Submission to a competition.\nc. A “Final Submission” is the Submission selected by the user, or automatically selected by Kaggle in the event not selected by the user, that is/are used for final placement on the competition leaderboard.\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.\ne. The “Private Leaderboard” is a ranked display of Participants’ Submission scores against the private test set. The Private Leaderboard determines the final standing in the competition.\nf. The “Public Leaderboard” is a ranked display of Participants’ Submission scores against a representative sample of the test data. This leaderboard is visible throughout the competition.\ng. A “Sponsor” is responsible for hosting the competition, which includes but is not limited to providing the data for the competition, determining winners, and enforcing competition rules.\nh. A “Submission” is anything provided by the Participant to the Sponsor to be evaluated for competition purposes and determine leaderboard position. A Submission may be made as a model, notebook, prediction file, or other format as determined by the Sponsor.\ni. A “Team” is one or more Participants participating together in a Kaggle competition, by officially merging together as a Team within the competition platform.\n\n [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n [2]: http://www.kaggle.com/terms\n [3]: http://www.opensource.org\n [4]: http://www.opensource.org\n [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n [6]: http://www.kaggle.com/privacy\n [7]: http://www.kaggle.com/contact\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }