README.md

---
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:** included in this repo under [`checkpoints/`](./checkpoints)

> **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)
checkpoints/            # Pretrained baseline checkpoints (see "Baseline checkpoints" below)
```

## Baseline checkpoints

Pretrained checkpoints for all baselines are included under `checkpoints/`:

| Folder | Task | Notes |
|---|---|---|
| `checkpoints/intention/` | Pedestrian crossing **intention** | Image-based; variants: `c3d`, `conv_lstm`, `i3d`, `mask_pcpa`, `pcpa`, `sf_gru`, `single_rnn`, `static` |
| `checkpoints/piefull/` | PIE trajectory | Sub-models: `intention/`, `speed/`, `trajectory/` |
| `checkpoints/mtn/` | MTN trajectory | `epoch_latest.pth` |
| `checkpoints/bitrap/` | BiTraP trajectory | `epoch_latest.pth` |
| `checkpoints/sgnet/` | SGNet trajectory | `epoch_latest.pth` |

Download just the checkpoints with:

```bash
hf download Ruthvik9/IDD-PeD --repo-type dataset --include "checkpoints/*" --local-dir .
```

See the [GitHub README](https://github.com/Ruthvik9/IDD-PeD) for per-model setup and the exact
test/train commands.

## 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}
}
```