RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments
This addresses the problem of efficient and accurate tracking in complex traffic environments for applications like autonomous driving, though it appears incremental as it builds on tracking-by-detection with a new motion model.
The paper tackles real-time tracking of heterogeneous road agents in dense traffic videos, achieving 75.8% accuracy and outperforming prior state-of-the-art by at least 5.2% while operating at 30 fps.
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.