LiDAR-based object detection, tracking and prediction

This project aims at detecting, tracking and predicting movement of objects, especially pedestrian, using LiDAR data. Please learn more about it in the report.

Installation

All required packages are listed in requirements.txt. We recommend using a conda virtual environment to install them using the following command.

conda create --name <env> --file requirements.txt

Usage

1. The learning-based detection can be run using the following command:

python detect_PCAP_learning.py --class 0 --weights best.pt --conf-thres=0.4 --source Ouster-YOLOv5-sample.pcap --metadata-path Ouster-YOLOv5-sample.json  --view-img

2. The geometry-based detection can be run using the following command:

python detect_pcap_geometry.py --source Ouster-YOLOv5-sample.pcap --metadata-path Ouster-YOLOv5-sample.json

3. The results are saved in runs (resp. runs2) and PNG (resp. PNG2) for the learning-based (resp. geometry-based) approach. To animate the PGN files into a video, run:

python animate_png.py

The results are saved in animations.

References

This project was done by Virgile Foussereau and Jyh-Chwen Ko during a Computer Vision course given by Mathieu Brédif at École Polytechnique. The learning-based method is based upon a work on social distancing done by Fisher Jianyu.