--- pretty_name: UVH-26 (Urban Vision Hackathon Dataset) license: cc-by-4.0 tags: - computer-vision - object-detection - traffic - vehicles - india - cctv task_categories: - object-detection task_ids: - vehicle-detection language: - und annotations_creators: - crowd-sourced - expert-generated source_datasets: [] size_categories: - 10K UVH-26 is a large-scale India-specific traffic-camera dataset released by AIM@IISc. It contains 26,646 1080p images from ~2,800 Bengaluru Safe-City CCTV cameras over ~4 weeks, annotated via a nationwide crowdsourced hackathon (565 students) with ~1.8M bounding boxes across 14 vehicle classes. Two consensus versions are provided: Majority Voting and STAPLE. homepage: https://huggingface.co/datasets/iisc-aim/UVH-26/tree/v1.0 --- # Dataset Card for UVH-26 (Urban Vision Hackathon Dataset) ## Dataset Summary **UVH-26** is a large-scale, India-specific traffic-camera image dataset released by **AIM @ IISc** for research in intelligent transportation systems and vehicle detection. It contains **26,646** high-resolution (1080p) frames sampled from ≈ 2,800 Bengaluru *Safe City* CCTV cameras over a 4-week period. Images were annotated through a nationwide crowdsourced hackathon involving **565 college students**, producing **≈ 1.8 million bounding boxes** across **14 fine-grained vehicle classes** representative of Indian traffic conditions. To capture different levels of annotation consensus, UVH-26 includes **two separate annotation sets**: 1. **`UVH-26-MV`** — final labels computed via *majority voting* across multiple annotators per image. 2. **`UVH-26-ST`** — labels generated using the *STAPLE* algorithm (an Expectation–Maximization–based probabilistic consensus method) for higher reliability. These versions share identical image data but differ in bounding box consensus logic. ## Dataset Structure The datasets released follow the folder structure described below. ### **1. UVH-26-Train/** Contains **80% of the UVH-26 dataset** used for training. * **`images/`** – Training images organized into subfolders (`000/`, `001/`, …) for convenience. * `images/000/*` – Actual training images (`1.png`, `2.png`, …). Each image filename is unique across the entire dataset. * `images/001/*`, etc. – Additional subfolders following the same structure. * **`UVH-26-MV-Train.json`** – Majority Voting consensus annotations for training images in **COCO JSON format**. * **`UVH-26-ST-Train.json`** – STAPLE consensus annotations for training images in **COCO JSON format**. ### **2. UVH-26-Val/** Contains **20% of the UVH-26 dataset** used for validation. * **`images/`** – Validation images organized into subfolders (`000/`, `001/`, …). * `images/000/*` – Actual validation images. All filenames are globally unique across both training and validation sets. * `images/001/*`, etc. – Additional subfolders following the same structure. * **`UVH-26-MV-Val.json`** – Majority Voting consensus annotations for validation images in **COCO JSON format**. * **`UVH-26-ST-Val.json`** – STAPLE consensus annotations for validation images in **COCO JSON format**. ## Annotation JSON Schema Each annotation file follows the standard COCO structure: - **`images`** — list of image metadata `id`, `file_name`, `width`, `height` - **`annotations`** — object instances `id`, `image_id`, `category_id`, `bbox [x, y, width, height]`, `area` - **`categories`** — class taxonomy (IDs and names below) ### Annotation Pipeline - **Source:** frames captured between 06:00 – 18:00 IST during February 2025 - **Pre-annotation:** generated using a fine-tuned **RT-DETR v2-X** model trained on ≈ 3 k expert-labeled images - **Crowdsourcing:** > 550 student volunteers corrected or validated predictions through a gamified web interface with leaderboards - **Consensus:** both *majority voting* and *STAPLE* algorithms applied to derive final annotations ## Vehicle Classes | ID | Class Name | Description | | -- | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | | 1 | Hatchback | Small passenger cars without a protruding rear boot (“dickey”). | | 2 | Sedan | Passenger cars with a low-slung design and a separate protruding rear boot (“dickey”). | | 3 | SUV | Car-like vehicles with high ground clearance, a sturdy body, and no protruding boot. | | 4 | MUV | Large vehicles with three seating rows, combining passenger and cargo functionality. | | 5 | Bus | Large passenger vehicles used for public or private transport, including office shuttles and intercity buses. | | 6 | Truck | Heavy goods carriers with a front cabin and a rear cargo compartment. | | 7 | Three-wheeler | Compact vehicles with one front wheel and two rear wheels, featuring a covered passenger cabin. | | 8 | Two-wheeler | Motorbikes and scooters for single or double riders. Bounding boxes include both vehicle and rider. | | 9 | LCV | Lightweight goods carriers used for short- to medium-distance transport. | | 10 | Mini-bus | Shorter, compact buses with fewer seats; larger than a Tempo Traveller, often featuring a flat front. | | 11 | Tempo-traveller | Medium-sized passenger vans with tall roofs and side windows; larger than vans but smaller than minibuses, with a protruding front. | | 12 | Bicycle | Non-motorized, manually pedalled vehicles including geared, non-geared, women’s, and children’s cycles. Bounding boxes include both vehicle and rider. | | 13 | Van | Medium-sized vehicles for transporting goods or people, typically with a flat front and sliding side doors; smaller than Tempo Travellers. | | 14 | Other | Vehicles not covered in other classes, including agricultural, specialized, or unconventional designs. | ## Collection and Processing - **Source:** ≈ 2,800 *Safe City* surveillance cameras operated by Bengaluru Police - **Coverage:** both junction and mid-block perspectives across multiple city zones - **Selection:** images with high vehicle density, occlusion, and diverse viewpoints prioritized ## Intended Uses - Building accurate, lightweight, edge-deployed perception systems for **Intelligent Transportation Systems (ITS)** - Training and benchmarking vehicle detection models ## License - **Dataset:** [CC BY 4.0 International](https://creativecommons.org/licenses/by/4.0/) - **Pre-trained Models:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Acknowledgements We thank the **Bengaluru Traffic Police (BTP)** and the **Bengaluru Police** for providing access to the *Safe City* camera data from which the image datasets used for this release were derived. We thank **Capital One** for sponsoring the prizes for the **Urban Vision Hackathon** competition. We thank **IISc’s AI and Robotics Technology Park (ARTPARK)** and the **Centre for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP)** for funding the annotation and model-training efforts, and the **Kotak IISc AI-ML Centre (KIAC)** for providing the GPU resources required to train the models. We acknowledge the outreach support provided by the **ACM India Council** and the **IEEE India Council** to encourage chapter volunteers to participate in the hackathon. Lastly, we thank the **AI Centers of Excellence (AI COE)** initiative of the **Ministry of Education**, their **Apex Committee members**, and the **AIRAWAT Research Foundation**, whose support helped catalyze these efforts. Created by the **AI for Integrated Mobility (AIM)** group at the **Indian Institute of Science (IISc)**, Bengaluru.