Using Conditional Random Field for Crowd Behavior Analysis

The governing behaviors of individuals in crowded places offer unique and difficult challenges. In this paper, a novel framework is proposed to investigate the crowd behaviors and to localize the anomalous behaviors. Novelty of the proposed approach can be revealed in three aspects. First, we introduce block-clips by sectioning video segments into non-overlapping patches to marginalize the arbitrarily complicated dense flow field. Second, flow field is treated as a 2d distribution of samples in block-clips, which is parameterized by using mixtures of Gaussian keeping the generality intact. The parameters of each Gaussian model, particularly mean values are transformed into a sequence of Gaussian mean densities for each block-clip namely a sequence of latent-words. A bank of Conditional Random Field model is employed, one for each block-clip, which is learned from the sequence of latent-words and classifies each block-clip as normal and abnormal. Experiments are conducted on two challenging benchmark datasets PETS 2009 and University of Minnesota and results show that our method achieves higher accuracy in behavior detection and can effectively localize specific and overall anomalies. Besides, a comparative analysis is presented with similar approaches which demonstrates the dominating performance of our approach.