A Discriminative Tracklets Representation For Crowd Analysis

In this work, we propose a discriminative tracklets representation for motion pattern extraction from crowded scene. The representation incorporates relative position, velocity, and direction information of tracklet into one compact form by shaping it within a rectangle. We adopt deep belief networks to extract low-dimensional features from this representation. It not only reduces the computational complexity for the following clustering, but also achieves more discriminative tracklets representation which is invariant to noises brought by tracking failures. To determine the spatio-temporal distribution of each motion pattern, a robust clustering scheme composed of three clustering procedures is proposed. Comprehensive experiments in multiple datasets validate the effectiveness of our approach.