Improve dataset card: add paper, code, project links and sample usage

README.md

@@ -1,9 +1,12 @@
 ---
+language:
+- en
 license: cc-by-nc-4.0
+size_categories:
+- 1K<n<10K
 task_categories:
 - video-classification
-language:
-- en
+pretty_name: MOTOR
 tags:
 - MOTOR
 - driving
@@ -11,40 +14,42 @@ tags:
 - multimodal
 - eye-tracking
 - gaze
-- indian-traffic 
-pretty_name: MOTOR
-size_categories: 
-- 1K<n<10K
+- indian-traffic
 ---
 
 # MOTOR — A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding
 
-**Paper:** *MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding*, ICRA 2026.
-
-## Abstract
+[**Project Page**](https://varuniiith.github.io/MOTOR-Dataset/) | [**Paper**](https://huggingface.co/papers/2605.22550) | [**Code**](https://github.com/VarunIIITH/MOTOR)
 
-Two-wheelers account for a disproportionately high share of road fatalities in the Global South. Research on two-wheeler rider behavior, however, lags far behind four-wheelers, where multimodal datasets have driven major advances in Advanced Driver Assistance Systems (ADAS). To address this gap, we present the MOtorized TwO-wheeler Rider (MOTOR) dataset, the first large-scale, multi-view, multimodal resource dedicated to two-wheelers in dense, unstructured traffic. MOTOR 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). Rich annotations capture traffic context, rider state, 12 riding maneuvers spanning conventional and unconventional behaviors, and legality labels (Legal, Illegal, Unspecified). We benchmark rider behavior recognition and maneuver legality classification using state-of-the-art video action recognition backbones (CNN and Transformer-based), extended with multimodal fusion, and find that combining RGB, gaze, and telemetry consistently yields the best performance. MOTOR thus provides a unique foundation for advancing safety-critical understanding of two-wheeler riding. It offers the research community a benchmark to develop and evaluate models for behavior analysis, legality-aware prediction, and intelligent transportation systems.
+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).
 
-## Structure
+## Sample Usage
 
-annotations.csv                              # one row per clip
+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
+```
 
-clips/<ride>/front/<clip>.mp4                # front-facing road view
+### 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.
@@ -62,6 +67,19 @@ Two rides (`14`, `15`) do not include the eye-tracker modality. One ride (`12`)
 
 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.