# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
###### Abstract
The literature [3; 4; 5; 6; 7; 8] represents the progress of different stages in the field of abnormal behavior recognition of crowds. It is worth noting that there are three deficiencies in the existing literature research. (1) The analysis of abnormal behaviors in the literature is not comprehensive, and piece of the each literature only focuses on the research results of a specific development stage; (2) The pedestrian abnormal behavior detection technology has not been systematically included, and the key factors such as the characteristics and limitations of various methods have not been compared in detail; (3) For the visual problems that are common in complex scenes, such as dense crowds and severe occlusion between individuals, little of the existing literature has proposed targeted and efficient visual occlusion resolution strategies. Therefore, given the above deficiencies, this paper conducts a comprehensive and systematic analysis of the automatic recognition technology of abnormal behaviors of crowds in the field of computer vision, so that researchers in this field can better grasp the current situation and development direction of abnormal behavior detection of crowds. The main contributions of this paper are summarized as follows:  
(1) Systematic summary. This study summarizes the traditional methods of abnormal behavior detection systematically. Then we deeply explore how to use mathematical models to capture and analyze key characteristics such as the speed, direction, abnormal movement of individuals and the spatial layout, flow of people, flow speed, and direction of the group;  
(2) Comprehensive method categorization. Starting from the design concept and focus of the algorithm core, the abnormal behavior detection methods based on deep learning are divided into five categories. In addition, for the scale change and occlusion problems in the crowd, this paper provides a novel dynamic pedestrian centroid model and a visual occlusion resolution strategy;  
(3) Technical tools recommendation. We recommend four types of representative software tools and provide an experimental platform and practical tool reference for researchers in related fields;(4) Character analysis of bench-mark datasets. We analyze four international benchmark datasets of typical abnormal behaviors of crowds. Then we compare these datasets in detail from multiple perspectives such as scale, annotation, and main uses; and prospect the future development direction of abnormal behavior recognition from five aspects such as multi-modal fusion, multi-source multi-dimensional data fusion, and metaverse evolution model. The rest of this paper is structured as follows: Section 2 gives the definitions of crowd levels and abnormal behaviors; Section 3 introduces the main challenges faced by the abnormal behavior recognition task in the dense crowd scene; Section 4 comprehensively summarizes the methods of abnormal behavior recognition from the two dimensions of traditional methods and deep learning and further introduces the current mainstream software tools; and Section 5 introduces the datasets widely used in the field of abnormal behavior detection at home and abroad and the performance indicators of each algorithm on these datasets. Section 6 summarizes this paper and presents the future development trend of this research field. The article framework is shown in Figure 1.