Abnormal Behavior Recognition Based on Key Points of Human Skeleton
Yuchao Liu, Sunan Zhang, Ziyue Li, Yunpu Zhang
Abstract
Human action recognition is one of the most popular fields of computer vision. However, the traditional manual feature-based method, with large background interference, can hardly establish an accurate human model and the deep learning-based method runs slowly with huge amount of parameters. In this paper, we propose a new method which combination of the two. First, we extract time series human 3D skeleton key points by Yolo v4 and apply Meanshift target tracking algorithm; then convert key points into spatial RGB and put them into multi-layer convolution neural network for recognition. This method has a high recognition rate and fast recognition speed in a variety of environment such as enclosed environment and public scene. It can quickly identify holding guns, armed attacks, throwing, climbing, approaching and other abnormal behavior.