Robust People Tracking with Global Trajectory Optimization

Given three or four synchronized videos taken at eye level and from different angles, we show that we can effectively use dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions. In addition, we also derive metrically accurate trajectories for each one of them. Our main contribution is to show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another. In this way, we achieve robustness by finding optimal trajectories over many frames while avoiding the combinatorial explosion that would result from simultaneously dealing with all the individuals.