Robust multi-target tracking in outdoor traffic scenarios via persistence topology based robust motion segmentation

In this paper, we present a motion segmentation based robust multi-target tracking technique for on-road obstacles. Our approach uses depth imaging information, and integrates persistence topology for segmentation and min-max network flow for tracking. To reduce time as well as computational complexity, the max flow problem is solved using a dynamic programming algorithm. We classify the sensor reading into regions of stationary and moving parts by aligning occupancy maps obtained from the disparity images and then, incorporate Kalman filter in the network flow algorithm to track the moving objects robustly. Our algorithm has been tested on several real-life stereo datasets and the results show that there is an improvement by a factor of three on robustness when comparing performance with and without the topological persistent detections. We also perform measurement accuracy of our algorithm using popular evaluation metrics for segmentation and tracking, and the results look promising.