Learning Major Pedestrian Flows in Crowded Scenes

We present a crowd analysis approach computing a representation of the major pedestrian flows in complex scenes. It treats crowds as a set of moving particles and builds a spatio-temporal model of motion events. A Growing Neural Gas algorithm encodes optical flow particle trajectories as sequences of local motion events and learns a opology which is the base for trajectory distance computations. Trajectory prototypes are aligned with a two-open-ends version of Dynamic Time Warping to cope with fragmented trajectores. The trajectories are grouped into an automatically determined number of clusters with self-tuning spectral clustering. The clusters are compactly represented with the help of Principal Component Analysis, providing a technique for unusual motion detection based on residuals. We demonstrate results for a publicly available crowded video and a scene with volunteers moving according to defined origin-destination flows.