Real-time detection algorithm of abnormal behavior in crowds based on Gaussian mixture model

Recently, abnormal evens detection in crowds has received considerable attention in the field of public safety. Most existing studies do not account for the processing time and the continuity of abnormal behavior characteristics. In this paper, we present a new motion feature descriptor, called the sensitive movement point (SMP). Gaussian Mixture Model (GMM) is used for modeling the abnormal crowd behavior with full consideration of the characteristics of crowd abnormal behavior. First, we analyze the video with GMM, to extract sensitive movement point in certain speed by setting update threshold value of GMM. Then, analyze the sensitive movement point of video frame with temporal and spatial modeling. Identify abnormal behavior through the analysis of mutation duration occurs in temporal and spatial model, and the density, distribution and mutative acceleration of sensitive movement point in blocks. The algorithm can be implemented with automatic adapt to environmental change and online learning, without tracking individuals of crowd and large scale training in detection process. Experiments involving the UMN datasets and the videos taken by us show that the proposed algorithm can real-time effectively identify various types of anomalies and that the recognition results and processing time are better than existing algorithms.