Recognizing violent activity without decoding video streams

The processes of motion target detection and tracking in most of traditional activity recognition methods are usually complicated and the application of these methods is limited. In this paper, we propose a fast violent activity recognition method based on motion vectors. First, we extract the motion vectors from compressed video data directly. Then, we analyze the features of the motion vectors in each frame and between frames, and get Region Motion Vectors descriptor (RMV). Finally, we use the Support Vector Machine (SVM) which takes the radial basis as the kernel function to classify the RMV and determine whether the violent activity exists in the video or not. Experimental results on several datasets have shown that the proposed method can detect 96.1% of the violent activities in videos (false probability is about 5.1%), and the calculation speed is very fast, which means the new method can be used in embedded systems.