Abnormal behavior detection in videos using deep learning

Abstract

A new method for abnormal behavior detection is proposed using deep learning. Two SDAEs are utilized to automatically learn appearance feature and motion feature respectively, which are constrained in the space–time volume along dense trajectories that carry rich motion information to reduce the computational complexity. The vision words are exploited to describe behavior by the bag of words, and in order to reduce feature dimensions, the Agglomerative Information Bottleneck approach is used for vocabulary compression. An adaptive feature fusion method is adopted to enhance the discriminating power of these features. A sparse representation is exploited to abnormal behavior detection, which improve the detection accuracy. The proposed method is verified on the public dataset BEHAVE and BOSS and the results indicate the effectiveness of the proposed method.