Abnormal Event Detection in Surveillance Video: A Compressed Domain Approach for HEVC
Yihao Zhang, Hongyang Chao · 2017
Recently, detecting abnormal events in surveillance videos has become one of the most important tasks of video analysis. There is a huge demand in developing fast and accurate abnormal event detection approach. However, traditional pixel-domain approaches are time-consuming and require fully decoding of the bit streams. On the other hand, the compression format may provide useful information to solve the challenge: compared to raw pixels, the advantage of the compression format is that it already contains some valuable clues for video analysis. In this work, we focus on anomaly detection in traffic video with the information provided in the HEVC compressed domain. There are mainly two contributions. The first is that we propose a novel feature namely motion energy intensity (MEI) to represent the motion intensity within coding unit, based on the MV fields, bit allocations and partitions of coding units in the HEVC compressed domain, which can provide energy and motion information of the LCU. The second contribution is that we have proposed an anomaly detection model based on the MEI. Furthermore, this approach can also serve as preprocess for pixel domain detection algorithms, by labeling suspicious part of the video which are likely to be abnormities. Figure 1 shows the flowchart of the proposed algorithm. Experimental results show that the proposed approach can obtain rather high detecting accuracy with very little overhead. The processing speed for detection is as fast as 1200 fps on 480x360 video. When served as a preprocess approach, the pixel-domain methods may also benefit with up to 50% time reduction.