基于骨架序列提取的异常行为识别

视频监控系统中的人员异常行为识别研究具有重要意义.针对传统算法检测实时性和准确性差,易受环境影响的问题,提出一种基于骨架序列提取的异常行为识别算法.首先,改进YOLOv3网络用以对目标进行检测、结合RT-MDNet算法进行跟踪,得到目标的运动轨迹;然后,利用OpenPose模型提取轨迹中目标的骨架序列;最后通过时空图卷积网络结合聚类对目标进行异常行为识别.实验结果表明,在存在光照变化的复杂环境下,算法识别准确率达94%,处理速度达18.25 fps,能够实时、准确地识别多种目标的异常行为.

Recognition of Abnormal Behavior Based on Skeleton Sequence Extraction

The research on the recognition of abnormal human behavior in video surveillance systems is of great significance. As traditional algorithms are easily affected by the environment and have poor timeliness and accuracy, an abnormal behavior recognition algorithm based on skeleton sequence extraction is proposed. Firstly, the improved YOLOv3 network is used to detect targets and is combined with the RT-MDNet algorithm to track them for target trajectories. Then, the OpenPose model is employed to extract the skeleton sequence of targets in the trajectories. Finally, the spatiotemporal graph convolutional network combined with clustering is applied to recognize the abnormal behavior of the targets. The experimental results indicate that the proposed algorithm has a processing speed of 18.25 fps and recognition accuracy of 94% under a complex background of light changes, which can accurately identify the abnormal behavior of various targets in real time.