RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2019
- DOI:
- 10.48550/arXiv.1906.10712
- arXiv:
- arXiv:1906.10712
- Bibcode:
- 2019arXiv190610712C
- Keywords:
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- Computer Science - Robotics
- E-Print:
- Final pre-print. Accepted at ICRA 2020