RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments.

Paper Code Dataset Tech Report
RoadTrack GitHub Repository TRAF/MOT/KITTI Tech Report

The first video is a short summary of our work. The second video demonstrates RVO. The final video demonstrates the SimCAI algorithm.

We present a realtime tracking algorithm, RoadTrack, to track heterogeneous road-agents in dense traffic videos. Our approach is designed for traffic scenarios that consist of different road-agents such as pedestrians, two-wheelers, cars, buses, etc. sharing the road. We use the tracking-by-detection approach where we track a road-agent by matching the appearance or bounding box region in the current frame with the predicted bounding box region propagated from the previous frame. Roadtrack uses a novel motion model called the Simultaneous Collision Avoidance and Interaction (SimCAI) model to predict the motion of road-agents by modeling collision avoidance and interactions between the road-agents for the next frame. We demonstrate the advantage of RoadTrack on a dataset of dense traffic videos and observe an accuracy of 75.8% on this dataset, outperforming prior state-of-the-art tracking algorithms by at least 5.2%. RoadTrack operates in realtime at approximately 30 fps and is at least 4 times faster than prior tracking algorithms on standard tracking datasets.

Please cite our work if you found it useful,

  title={RoadTrack: Tracking Road Agents in Dense and Heterogeneous Environments},
  author={Chandra, Rohan and Bhattacharya, Uttaran and Randhavane, Tanmay and Bera, Aniket and Manocha, Dinesh},
  journal={arXiv preprint arXiv:1906.10712},