A Review on Computer Vision-Based Methods for Abnormal Human Action Recognition

Abstract

Human behavior recognition constitutes a crucial research domain within both computer vision and behavior recognition. As a branch of human behavior recognition, abnormal behavior recognition has witnessed rapid advancement in recent years, which is capable of enhancing the governance level of public safety. To investigate the theoretical and technical progress of abnormal behavior recognition in public places within the realm of computer vision, this paper initially delineates the definition of abnormal behavior in public places. Secondly, as computer vision and pattern recognition technologies have progressed, algorithms are now divided into two distinct categories: traditional methods and those leveraging deep learning techniques. The identification of atypical human behavior can be divided into two approaches depending on the detection of body key points: one approach uses skeletal key points, while the other focuses on analyzing temporal and spatial features. Finally, this paper conducts a review of the mainstream datasets of abnormal human behavior both at home and abroad, analyzes the performance of related algorithms on the datasets, and gives future research directions as well as optimization suggestions.