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We present a realtime tracking algorithm, RoadTrack, to track heterogeneousroad-agents in dense traffic videos. Our approach is designed for trafficscenarios that consist of different road-agents such as pedestrians,two-wheelers, cars, buses, etc. sharing the road. We use thetracking-by-detection approach where we track a road-agent by matching theappearance or bounding box region in the current frame with the predictedbounding box region propagated from the previous frame. RoadTrack uses a novelmotion model called the Simultaneous Collision Avoidance and Interaction(SimCAI) model to predict the motion of road-agents by modeling collisionavoidance and interactions between the road-agents for the next frame. Wedemonstrate the advantage of RoadTrack on a dataset of dense traffic videos andobserve an accuracy of 75.8% on this dataset, outperforming priorstate-of-the-art tracking algorithms by at least 5.2%. RoadTrack operates inrealtime at approximately 30 fps and is at least 4 times faster than priortracking algorithms on standard tracking datasets.

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