Research On Collective Behavior Recognition Algorithm Based On Flow Density Peak

Collective behavior refers to the motion pattern of a group of individuals with similar movement in the video scene, which is a common phenomenon in nature. The study of collective behavior is closely related to human life, and has attracted much attention of many scholars from different fields. The recognition of collective behavior is also one of the key problems in the field of computer vision. The collective behavior in the video scene is composed of many sub-groups with different shapes, uneven density, and motion consistency, these sub-groups often have mutual interaction. At the same time, the interaction between any two sub-groups is not single, but there are a variety of interactions. This kind of group behavior pattern, which is composed of sub-groups with multiple interactions, is referred to as the multiple interactive collective behavior. The difficulties and challenges in the recognition of multiple interactive collective behaviors: 1) How to measure the motion consistency between the individual and the neighbors to solve the diversity of the local sub-groups shape and the uneven density of the individual, to ensure the accuracy of the local sub-groups recognition; 2) How to describe the multiple interaction between local sub-groups in order to ensure the recognition of the collective behavior with global consistency; 3) How to ensure the efficiency of the whole process of multiple interactive collective behavior recognition.In this paper, a large number of experiments, analysis and research are carried out for the difficulties and challenges in the identification of the multiple interactive collective behavior, Flow Density based Multiple Interaction collective behavior recognition algorithm(FDMI) is proposed. The work done is as follows:(1) Based on the fluid mechanics, the fluid viscous stress model is translated into the video collective behavior, and we define the flow density of the collective behavior of the video. It reveals the motion consistency between the individual and neighbors in the video scene, and solves the diversity of the local sub-groups shape and the uneven density of the individual, which ensures the accuracy of the local sub-groups recognition;(2) Based on the graph theory, this paper establishes the Multiple Adjacency Relation Model(MARM). It models the multiple interaction of the collective behavior in the video scene, and ensures the recognition of collective behavior with the global consistency;(3) In this paper, we propose Flow Density based Multiple Interaction behavior recognition algorithm(FDMI). The algorithm automatically determines the number of cluster centers of mass, and accurately recognizes the abnormal points. Under the premise of ensuring the high efficiency, the recognition accuracy of the multi interactive collective behavior in the video is further improved.Experiments are carried out on various scenarios on different data sets. The accuracy and efficiency of the method are verified by comparing the experimental results with the existing methods in two aspects of qualitative and quantitative. At the same time, the parameter values of the FDMI algorithm are discussed.