Detect coherent motions in crowd scenes based on tracklets association

Coherent motion is a very common motion pattern in crowded scenes. Coherent Filter is a very effective and robust tool to detect coherent motions based on point trajectories, the performance of coherent filter depends on point trajectories' property. In this work, we present a two-stage strategy to extract dense, accurate and long-term point trajectories from crowded scenes. The method includes a tracklets acquisition procedure and a tracklets association procedure. We use LDOF tracker to acquire dense tracklets, and then formulate tracklets association as a linear assignment problem (LAP). Experiments conducted on challenging crowd datasets show that our trajectories are very suitable for detecting coherent motions in crowded scenes.