Abnormal Behavior Recognition Based on Key Points of Human Skeleton. Issue 5 (2020)

• Record Type: Journal Article
• Authors: Liu, Yuchao; Zhang, Sunan; Li, Ziyue; Zhang, Yunpu
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 5(2020)
• Page Start: 441
• Page End: 445
• Publication Date: 2020
• Subjects: Behavior recognition -- Object recognition -- Object tracking -- 3D skeleton key points; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.04.120
• Languages: English
• ISSNs: 2405-8963

Abstract

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.