| The research wortk of this paper focuses on the detectior algorithm of crowd abnormal behavior in public places.Ih this paper,most of the work focus on four key problems in the process of the design and implementation of the group anomaly dctection method,which are the feature point extraction algorithm,motion forground dctcction,target tracking and abnormal behavior detection.Most of the existing crowd anomaly detecion algorithms uses the training template.In this paper,a method of crowd anomaly detection based on statistical analysis of feature points is designed.Firstly,through the research of the Harris feature points extraction algorithm and the SIFT feature points extraction algorithm,an improved Harris feature extraction methed based on multi scale space is designed,which combines the advantages of Harris feature points and SIFT feature points,After the feature points are extracted by the method,The nest work is to select the feature points of the moving foreground region to improve the accuracy of anomaly detection.For this purpose,through the research of the moving foreground extraction algorithm,the feature points optimization method based on motion foreground extraction algorrithm is designed.then the next step is to track and match the optimimized feature points by Lucas-Kanade algorithm.And on this bads,further research is made to explore the spatial position relationship of the feature points in the adjacent video framees.According to the change of the spatial position,two motion attributes are attached to the matched feature points,which are called speed and direction.Then,the crowd stadium can be constructed based on classification and statistics of the motion attributes.In the crowd abnomal detection phase,on the basis of the statistical information of speed and direction,the speed abnomal judgment and the direction abnomal judgment are designed respectively.At the same time,the synthetic method of the two aspects of comprehensive speed and direction is also given.Finally,the method of crowd abnormal dection by using feature points is realized by programming on the Studio Visual 2010 development platform.The related performance test is carried out using UMN video data set.The test results show that this method has good performance in real-time and accuracy. |
