Add dataset card

README.md

@@ -0,0 +1,101 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+  - other
+tags:
+  - pedestrian-intention-prediction
+  - trajectory-prediction
+  - autonomous-driving
+  - unstructured-traffic
+  - video
+  - annotations
+pretty_name: IDD-PeD
+---
+
+# IDD-PeD: Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Environments
+
+IDD-PeD is a pedestrian behavior dataset for **intention** and **trajectory** prediction in
+**unstructured traffic environments**. It accompanies the paper *"Pedestrian Intention and
+Trajectory Prediction in Unstructured Traffic Using IDD-PeD"* (ICRA 2025).
+
+- **Project page:** https://cvit.iiit.ac.in/research/projects/cvit-projects/iddped
+- **Code (baselines + dataset interface):** https://github.com/Ruthvik9/IDD-PeD
+- **Baseline checkpoints:** https://huggingface.co/Ruthvik9/IDD-PeD-baselines
+
+> **License note:** This card defaults to `cc-by-nc-4.0`. Update the `license` field above if
+> your dataset uses a different license.
+
+## What's in this repository
+
+This repository hosts the **annotations** for IDD-PeD. The raw **videos** are large and are
+distributed separately from IIIT servers (links below).
+
+```
+annotations/            # Pedestrian spatial + behavioral + scene annotations (CVAT XML, per set/video) + annotated-frame CSVs
+annotations_vehicle/    # Vehicle annotations (CVAT XML)
+data_cache/
+  iddp_database.pkl     # Pre-built database cache used by the dataset interface (iddped_interface_traj.py)
+```
+
+## Downloading the videos
+
+The videos are distributed as 9 tar archives. Download them and arrange them under
+`data/IDDPedestrian/videos/gopro/` as expected by the code:
+
+```
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0001.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0002.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0003.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0004.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0005.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0006.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0007.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0008.tar
+https://cvit.iiit.ac.in/images/datasets/IDDPed/Videos/gp_set_0009.tar
+```
+
+A convenience script (`download_videos.sh`) is provided in the
+[GitHub repo](https://github.com/Ruthvik9/IDD-PeD).
+
+## Train / test split
+
+- **Train:** `gp_set_0001`, `gp_set_0002`, `gp_set_0004`, `gp_set_0006`, `gp_set_0007` (3284 pedestrians)
+- **Test:** `gp_set_0003`, `gp_set_0005`, `gp_set_0008`, `gp_set_0009` (1632 pedestrians)
+
+Roughly a 70/30 split.
+
+## Annotation types
+
+The dataset provides five annotation families for pedestrians requiring the ego-vehicle's attention:
+
+1. **Spatial** — tracked bounding boxes + occlusion levels (none / partial / full), object types
+   (pedestrian, vehicle, traffic light, crosswalk, bus station), and 17-keypoint 2D pose.
+2. **Behavioral** (frame-level, 6 attributes per pedestrian per frame) — crossing behavior,
+   traffic interaction, pedestrian activity, attention indicators, social dynamics, stationary behavior.
+3. **Scene** — intersection type, signalized type, road type, location type, motion direction, time of day.
+4. **Interaction** — pedestrian–ego-vehicle interaction flag.
+5. **Location** — spatial context (near divider, side of the road, near crosswalk, ...).
+
+Additional pedestrian attributes: age, gender, carrying object, crossing motive, crosswalk usage,
+and whether the pedestrian crosses in front of the ego-vehicle.
+
+See the [GitHub README](https://github.com/Ruthvik9/IDD-PeD) for the complete attribute taxonomy
+and per-attribute value definitions.
+
+## Usage
+
+The dataset interface (`iddped_interface_traj.py`) in the GitHub repo consumes these annotations.
+Place this repo's contents under `data/IDDPedestrian/` and follow the setup instructions in the
+repo to extract frames and run the baselines.
+
+## Citation
+
+```bibtex
+@inproceedings{idd2025ped,
+  author    = {Ruthvik Bokkasam and Shankar Gangisetty and A. H. Abdul Hafez and C. V. Jawahar},
+  title     = {Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD},
+  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
+  publisher = {IEEE},
+  year      = {2025}
+}
+```