Priority Tracking of Pedestrians for Self-Driving Cars

A priority tracking framework to enable scalable tracking of pedestrians for self-driving cars in dense scenes is developed. Reachability analysis is used on the ego-vehicle and pedestrian tracks to assign different tracking strategies based on perceived priority. Therefore, computationally heavy algorithms such as 3D multi-object tracking (MOT) can be performed on priority objects, while less relevant tracks are maintained by lower-level trackers. The approach is empirically evaluated in simulated and real traffic scenarios with dense detection of pedestrians. We show that our approach reduces the number of objects tracked by the 3D MOT by 5 fold when compared to the standard approach, and ensures the ego vehicle satisfies the same key criteria for passive safety.