Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos

Dynamic scene analysis has become a popular research area especially in video surveillance. The goal of this paper is to mine semantic motion patterns and detect abnormalities deviating from normal ones occurring in complex dynamic scenarios. To address this problem, we propose a data-driven and scene-independent approach, namely, Bilayer sparse topic model (BiSTM), where a given surveillance video is represented by a word-document hierarchical generative process. In this BiSTM, motion patterns are treated as latent topics sparsely distributed over low-level motion vectors, whereas a video clip can be sparsely reconstructed by a mixture of topics (motion pattern). In addition to capture the characteristic of extreme imbalance between numerous typical normal activities and few rare abnormalities in surveillance video data, a one-class constraint is directly imposed on the distribution of documents as a discriminant priori. By jointly learning topics and one-class document representation within a discriminative framework, the topic (pattern) space is more specific and explicit. An effective alternative iteration algorithm is presented for the model learning. Experimental results and comparisons on various public data sets demonstrate the promise of the proposed approach.