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arXiv · 1906.10712

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

Rohan Chandra·Uttaran Bhattacharya·Tanmay Randhavane·Aniket Bera·Dinesh Manocha
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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.

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Rohan Chandra, Uttaran Bhattacharya, Tanmay Randhavane, Aniket Bera, Dinesh Manocha. 2019-06-25. RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments. https://arxiv.org/abs/1906.10712

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