  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.

Robotics