A fast recognition algorithm for suspicious behavior in high definition videos

Detecting suspicious behavior from high definition (HD) videos is always a complex and time-consuming process. To solve that problem, a fast suspicious behavior recognition method is proposed based on motion vectors. In this paper, the data format and decoding features of HD videos are analyzed. Then, the characteristics of suspicious activities and the ways of obtaining motion vectors directly from the video stream are concluded. Besides, the motion vectors are normalized by taking the reference frames into account. The feature vectors that display the inter-frame and intra-frame information of the region of interest are extracted. Gaussian radial basis function is employed as the kernel function of the support vector machines (SVM). It also realizes the detection and classification of suspicious behavior in HD videos. Finally, an extensive set of experiments are performed and this method is compared with some of the most recent approaches in the field using publicly available datasets as well as a new annotated human action dataset including actions performed in complex scenarios.