#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/motor_two_wheel_rider" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for motor_two_wheel_rider

![image/png](https://huggingface.co/datasets/Voxel51/motor_two_wheel_rider/resolve/main/motor_two_wheel_rider.gif)

MOTOR (MOtorized TwO-wheeler Rider) is the first large-scale, multi-view, multimodal dataset dedicated to understanding two-wheeler rider behavior in dense, unstructured traffic conditions typical of the Global South. The full dataset comprises **1,629 annotated sequences** (~25 hours) from **16 riders** collected across diverse traffic scenarios in India.

**This repository contains a subset of the MOTOR dataset** imported into FiftyOne format for easy exploration and analysis. Each clip in the FiftyOne dataset contains **4 synchronized camera views** (front-mounted, helmet-mounted, rear-mounted, and eye-tracker), organized as grouped samples for easy multi-view analysis. The dataset captures both **conventional riding behaviors** (going straight, turns, lane changes) and **unconventional behaviors** (weaving through traffic, obstruction avoidance, violations) with rich annotations including legality labels, traffic context, GPS trajectories, gyroscope data, vehicle speeds, and rider gaze patterns.

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 324 samples.

## Installation

If you haven’t already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/motor_two_wheel_rider")
```

To visualize GPS routes in the FiftyOne App Map panel, you need a Mapbox API key:

1. Sign up for a free Mapbox account at https://mapbox.com
2. Get your API token from https://account.mapbox.com/access-tokens/
3. Export the token before launching the App:

```bash
export MAPBOX_TOKEN=your_mapbox_token_here

# Launch the App
session = fo.launch_app(dataset)
```

### Dataset Sources

- **Curated by:** Varun Paturkar et al., IIIT Hyderabad
- **Paper:** [MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding](https://arxiv.org/abs/2605.22550) (ICRA 2026)
- **Repository:** https://github.com/varuniiith/MOTOR-Dataset
- **Project Page:** https://varuniiith.github.io/MOTOR-Dataset/
- **Hugging Face:** https://huggingface.co/datasets/varunpaturkar/MOTOR
- **License:** Research use only (as specified by authors)

## Uses

### Direct Use

- **Rider behavior recognition**: Train models to classify 12 riding maneuvers
- **Legality prediction**: Predict whether maneuvers comply with traffic rules
- **Attention modeling**: Analyze rider gaze patterns during different behaviors
- **Multimodal fusion**: Combine video, gaze, and telemetry for improved predictions
- **Safety research**: Study dangerous behaviors and near-collision events
- **Traffic analysis**: Understand two-wheeler behavior in dense traffic conditions
- **ADAS development**: Build advanced driver assistance systems for motorcycles

### Out-of-Scope Use

- **Real-time inference**: Clips are short (~1-20 seconds) and pre-segmented
- **Autonomous driving**: Dataset focuses on rider behavior, not scene understanding
- **Non-Indian traffic**: Traffic patterns are specific to dense, unstructured Indian roads
- **Four-wheeler analysis**: Dataset is specific to two-wheelers (motorcycles, scooters)

## Dataset Structure

### FiftyOne Organization

The dataset is organized as a **grouped dataset** with 4 slices per clip. You can switch between camera views in the FiftyOne App using the group slice selector, or programmatically by setting `dataset.group_slice`:

- `front` - Front-mounted camera (default) - includes GPS routes and telemetry
- `helmet` - Helmet-mounted camera
- `rear` - Rear-mounted camera
- `eye_tracker` - Eye tracker with rider’s POV - includes gaze heatmaps

### Sample-Level Fields

Each sample contains the following fields:

| Field              | Type               | Description                                   |
|--------------------|--------------------|-----------------------------------------------|
| `clip_id`          | String             | Unique clip identifier (e.g., “01_042”)       |
| `video_id`         | Integer            | Full ride video ID                            |
| `camera`           | String             | Camera view: front, helmet, rear, eye_tracker |
| `source_timestamp` | String             | Original position in full ride (MM:SS-MM:SS)  |
| `duration_s`       | Float              | Clip duration in seconds                      |
| `location`         | GeoLocations       | GPS route polylines (front slice only)        |
| `event`            | TemporalDetections | Primary riding behavior                       |
| `legality`         | TemporalDetections | Legal/illegal/unspecified                     |
| `head_pose`        | TemporalDetections | Rider head direction                          |
| `road_type`        | TemporalDetections | Paved/unpaved                                 |
| `road_marking`     | TemporalDetections | Marked/unmarked lanes                         |
| `divider`          | TemporalDetections | Yes/no lane divider                           |
| `traffic_density`  | TemporalDetections | Low/medium/high                               |
| `n_lanes`          | TemporalDetections | Number of lanes                               |

### Frame-Level Fields

**On front slice** (telemetry):

- `gps`: GPS position as `GeoLocation(point=[lon, lat])` — supports frame-level geo queries
- `speed_2d_mps`: 2D speed in meters/second
- `speed_3d_mps`: 3D speed in meters/second
- `gyro_x`, `gyro_y`, `gyro_z`: Gyroscope readings (deg/s)
- `gps_alt_m`: GPS altitude in meters
- `telemetry_timestamp_s`: Telemetry timestamp in seconds

**On eye_tracker slice** (gaze):

- `gaze`: Gaussian heatmap (224×224) showing attention region

**Note on GPS:** The sample-level `location` field holds the full route polyline (`GeoLocations`) and is what the Map panel renders and what `geo_near`/`geo_within` query against. The frame-level `gps` field (`GeoLocation`) stores the position at each individual frame and supports per-frame location queries.

## Dataset Creation

### Curation Rationale

Two-wheelers account for a disproportionately high share of road fatalities in the Global South, yet research on rider behavior lags far behind four-wheeler ADAS research.

The MOTOR dataset addresses this gap by providing the first large-scale, multi-view, multimodal resource for understanding two-wheeler behavior in dense, unstructured traffic conditions typical of countries like India and Indonesia.

### Source Data

#### Data Collection

- **Platform**: Multi-camera setup with 3 GoPro Hero 10 cameras + eye-tracking glasses (Aria/Pupil)
- **Duration**: 4 weeks of data collection
- **Location**: Various roads in India (urban, highway, peak/off-peak hours)
- **Riders**: 16 riders with varying experience (2-20 years)
- **Vehicles**: Multiple two-wheeler types (motorcycles, scooters)
- **Synchronization**: All camera streams and sensors time-synchronized

#### Data Processing

- **Video**: 1920×1080, 30 FPS, with stabilization
- **Telemetry**: Extracted from GoPro recordings using GoPro telemetry extractor
- **Gaze**: Red gaze marker overlay burned into eye tracker video
- **Clips**: Pre-extracted to match event boundaries (typically 1-20 seconds)
- **Annotations**: Professional annotators trained on Indian Motor Vehicle Act (2017)

### Annotations

#### Annotation Process

Two professional annotators labeled all sequences under expert supervision:

1. First 50 sequences annotated independently by both annotators
2. Expert reviewed work, resolved discrepancies, provided feedback
3. Remaining sequences annotated individually with periodic random checks
4. Annotations include:
   - Riding maneuver classification (12 classes)
   - Legality determination based on Indian Motor Vehicle Act
   - Head pose direction (on road, left, right, either side)
   - Traffic context (road type, lanes, markings, density)

#### Who are the Annotators?

Professional annotators trained on maneuver definitions and traffic violation rules from the Indian Motor Vehicle Act (2017), working under expert supervision.

## Citation

**BibTeX:**

```bibtex
@inproceedings{paturkar2026motor,
  title={MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding},
  author={Paturkar, Varun and others},
  booktitle={2026 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2026}
}
```

**APA:**

Paturkar, V., et al. (2026). MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding. *2026 IEEE International Conference on Robotics and Automation (ICRA)*.