Automatically Determining Dominant Motions in Crowded Scenes by Clustering Partial Feature Trajectories

We present a system for automatically identifying dominant motions in a crowded scene. Accurately tracking individual objects in such scenes is difficult due to inter-and intra-object occlusions that cannot be easily resolved. Our approach begins by independently tracking low-level features using optical flow. While many of the feature point tracks are unreliable, we show that they can be clustered into dominant motions using a distance measure for feature trajectories based on longest common subsequences. Results on real video sequences demonstrate that the approach can successfully identify both dominant and anomalous motions in crowded scenes. These fully-automatic algorithms could be easily incorporated into distributed camera networks for autonomous scene analysis.