Research on Targets Classification in Video Surveillance

Video-based motion analysis is receiving increasing attention in the domain of computer vision.An object classification algorithm is proposed in this paper used for video surveillance,which can classify moving objects into predefined four categories: human being,crowd,car and bicyclist.In this paper,several simple shape features of moving objects are defined and the SVM(Support Vector Machines),which based on small samples statistical learning theory,is chosen to classify different objects.At last,to meet the real-time requirement,the method of alternative classification is presented and several other methods that can improve the efficiency of classification are described.Experiments show that the method can accurately distinguish human being,crowd,car,and bicyclist.