| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - video-classification |
| pretty_name: MOTOR |
| tags: |
| - MOTOR |
| - driving |
| - two-wheeler |
| - multimodal |
| - eye-tracking |
| - gaze |
| - indian-traffic |
| --- |
| |
| # MOTOR — A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding |
|
|
| [**Project Page**](https://varuniiith.github.io/MOTOR-Dataset/) | [**Paper**](https://huggingface.co/papers/2605.22550) | [**Code**](https://github.com/VarunIIITH/MOTOR) |
|
|
| MOTOR is the first large-scale, multi-view, multimodal resource dedicated to two-wheelers in dense, unstructured traffic. It comprises 1,629 annotated sequences (25+ hours of video data) collected from 16 riders and integrates synchronized front, rear, and helmet videos, rider eye-gaze from wearable trackers, on-road audio, and telemetry (GPS, accelerometer, gyroscope). |
|
|
| ## Sample Usage |
|
|
| To use this dataset with the official codebase, follow these steps: |
|
|
| ### Setup |
| ```bash |
| conda create -n motor python=3.10 -y |
| conda activate motor |
| pip install -r requirements.txt |
| ``` |
|
|
| ### Training |
| The following command trains a three-stream fusion model (RGB + gaze + telemetry) on a Swin3D-T backbone: |
| ```bash |
| python train.py --config configs/swin3dt_gaze_tele.yaml |
| ``` |
|
|
| ## Structure |
|
|
| ```text |
| annotations.csv # one row per clip |
| clips/<ride>/front/<clip>.mp4 # front-facing road view |
| clips/<ride>/helmet/<clip>.mp4 # helmet-mounted view |
| clips/<ride>/rear/<clip>.mp4 # rear-facing view |
| clips/<ride>/eye_tracker/<clip>.mp4 # rider's first-person gaze view |
| clips/<ride>/telemetry/<clip>.csv # per-frame gyro + GPS for the clip |
| ``` |
|
|
| Faces and license plates in all camera videos are blurred for privacy. |
| Two rides (`14`, `15`) do not include the eye-tracker modality. One ride (`12`) has GPS-only telemetry. |
|
|
| ## Annotation columns |
|
|
| `Video ID, Clip ID, Timestamp, Road Type, No. of Lanes, Road Marking, Divider, Traffic Density, Event, Head Pose Direction, Legality` |
|
|
| - **Event:** going straight, lane change, overtake, left/right turn, u-turn, weaving through traffic, obstruction avoidance, distracted driving, stop, violation, near collision |
| - **Legality:** legal / illegal — based on Indian Motor Vehicle Act and local conventions |
|
|
| ## Per-clip telemetry CSV schema |
|
|
| `frame, timestamp_s, gyro_x, gyro_y, gyro_z, gps_lat, gps_lon, gps_alt_m, speed_2d_mps, speed_3d_mps` |
|
|
| Resampled to 30 Hz; `timestamp_s` is clip-local. |
|
|
| ## Citation |
|
|
| If you find this work useful, please cite: |
|
|
| ```bibtex |
| @inproceedings{paturkar2026motor, |
| title={MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding}, |
| author={Paturkar, Varun and others}, |
| booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| Released under CC BY-NC 4.0 — non-commercial research use only. |
|
|
| ## Note |
|
|
| This release contains the annotated clip-level data. The full uncut raw rides (long-form multi-camera recordings together with their continuous telemetry) will be made publicly available soon. |
