--- pretty_name: BMD-45 (Bengaluru Mobility Dataset) license: cc-by-4.0 tags: - computer-vision - object-detection - traffic - vehicles - india - cctv - bengaluru - urban - intelligent-transportation-systems task_categories: - object-detection task_ids: - vehicle-detection language: - und annotations_creators: - crowd-sourced source_datasets: [] size_categories: - 10K 550 student volunteers corrected or validated predictions through a gamified web interface with leaderboards - **Consensus:** *majority voting* applied to derive final annotations ## Loading the Dataset ```python from datasets import load_dataset # Load from HuggingFace Hub ds = load_dataset("iisc-aim/BMD-45") # Access a sample sample = ds["train"][0] image = sample["image"] objects = sample["objects"] # {"bbox": [...], "categories": [...]} ``` ## Vehicle Classes | ID | Class Name | Description | | --- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | | 0 | Hatchback | Small passenger cars without a protruding rear boot ("dickey"). | | 1 | Sedan | Passenger cars with a low-slung design and a separate protruding rear boot ("dickey"). | | 2 | SUV | Car-like vehicles with high ground clearance, a sturdy body, and no protruding boot. | | 3 | MUV | Large vehicles with three seating rows, combining passenger and cargo functionality. | | 4 | Bus | Large passenger vehicles used for public or private transport, including office shuttles and intercity buses. | | 5 | Truck | Heavy goods carriers with a front cabin and a rear cargo compartment. | | 6 | Three-wheeler | Compact vehicles with one front wheel and two rear wheels, featuring a covered passenger cabin. | | 7 | Two-wheeler | Motorbikes and scooters for single or double riders. Bounding boxes include both vehicle and rider. | | 8 | LCV | Lightweight goods carriers used for short- to medium-distance transport. | | 9 | Mini-bus | Shorter, compact buses with fewer seats; larger than a Tempo Traveller, often featuring a flat front. | | 10 | Tempo-traveller | Medium-sized passenger vans with tall roofs and side windows; larger than vans but smaller than minibuses, with a protruding front. | | 11 | Bicycle | Non-motorized, manually pedalled vehicles including geared, non-geared, women's, and children's cycles. Bounding boxes include both vehicle and rider. | | 12 | Van | Medium-sized vehicles for transporting goods or people, typically with a flat front and sliding side doors; smaller than Tempo Travellers. | | 13 | Other | Vehicles not covered in other classes, including agricultural, specialized, or unconventional designs. | ## Baseline Results To justify the need for BMD-45, SOTA detectors were trained **on existing datasets** and evaluated on an expert-annotated reference set of 3,000 Bengaluru CCTV images. All models trained on other datasets fall well short of practical accuracy: | Model | Training Data | mAP@50:95 | | -------------- | ------------- | --------- | | D-FINE X | IDD | 0.46 | | RT-DETRv2 X | TrafficCAM | 0.39 | | Grounding DINO | Zero-shot | 0.13 | > These are **cross-dataset baselines** (models trained on other datasets, *not* BMD-45). Results for BMD-45-trained models are reported in the paper. The following figure shows per-class AP@50:95 for selected models trained on BMD-45 and evaluated on the BMD-45 validation split: ![AP@50:95 distribution for models trained on BMD-45 and evaluated on BMD-45 validation split](Figure-1.png) Models trained on BMD-45 also demonstrate cross-dataset generalization to **UA-DETRAC**, **IDD**, and **TrafficCAM** using a taxonomy-aware class mapping protocol (see paper for full results). ## Cross-Dataset Generalization BMD-45-trained models are evaluated on: - **UA-DETRAC** — highway fixed-camera dataset (China, 4 classes) - **IDD** — Indian Driving Dataset (ego-centric, 9 classes) - **TrafficCAM** — Indian CCTV dataset (9 classes, 4,400 frames) A taxonomy mapping protocol merges BMD-45's 14 fine-grained classes into the coarser categories of each target dataset for fair comparison. The following figures compare per-class AP@50:95 for models trained on BMD-45 versus models trained on IDD, UA-DETRAC, and TrafficCAM, all evaluated on the BMD-45 validation split: ![AP@50:95 distribution for models trained on BMD-45 and IDD, evaluated on BMD-45 validation split](Figure-2.png) ![AP@50:95 distribution for models trained on BMD-45 and UA-DETRAC, evaluated on BMD-45 validation split](Figure-3.png) ![AP@50:95 distribution for models trained on BMD-45 and TrafficCAM, evaluated on BMD-45 validation split](Figure-4.png) ## Comparison with Existing Datasets | Dataset | Venue | Task | View | Frames | Annotations | Classes | Cameras | Location | | ----------------- | --------------- | ----- | -------------- | ------- | ----------- | ------- | --------- | -------- | | IDD | WACV 2019 | D, S | Ego | 10K | 111.3K | 9 | — | IN | | UA-DETRAC | CVIU 2020 | D, M | Fixed | 140K | 1.21M | 4 | 24 | CN | | TrafficCAM | T-ITS 2025 | S | Fixed CCTV | 4.3K | 84.2K | 9 | NA | IN | | **BMD-45 (Ours)** | **CVPR-F 2026** | **D** | **Fixed CCTV** | **45K** | **481.9K** | **14** | **3,679** | **IN** | *D = Detection, S = Segmentation, M = Tracking; IN = India, CN = China* ## Collection & Processing Details - **Source:** ≈ 3,679 *Safe City* surveillance cameras operated by Bengaluru Police - **Coverage:** both junction and mid-block perspectives across multiple city zones - **Time period:** February 2025, daytime hours (06:00–18:00 IST) - **Resolution:** 1920 × 1080 RGB frames - **Selection:** images with high vehicle density, occlusion, and diverse viewpoints prioritized - **Filename obfuscation:** Image filenames are anonymized numeric IDs to prevent location inference ## Intended Uses - Training and benchmarking **vehicle detection models** for CCTV / fixed-camera deployment - Research in **Intelligent Transportation Systems (ITS)** for Indian and developing-world cities - Cross-dataset generalization studies for region-specific detection - Studying detection under **occlusion, heterogeneous traffic, and diverse viewpoints** ## 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.