Research Of Object Detection And Tracking Technologies For Intelligent Video Surveillance

Intelligentization of video surveillance system is an emerging research orientation in the field of computer vision. Its main goal is to realize the automatic analysis and judgment of the surveillance video sequences by computer vision technologies, to respond to the occurrence of the abnormal behavior under surveillance without delay. The object detection and tracking are the key technologies for intelligent video surveillance system, and they concern many fields such as digital image processing, machine learning, and artificial intelligence, etc. The research of object detection and tracking technologies has important practical significance in the field of security monitoring, traffic monitoring, and so on.This paper is committed to the key issues of moving object detection and tracking in intelligent video surveillance systems. On the basis of analysis and comparison of the object detection and tracking methods, and combined with the practical application, certain research and improvement of the methods are taken. The main research content is as follows:(1) In terms of moving objects detection, background subtraction, temporal differencing and optical flow are introduced briefly, and the advantages and disadvantages of the three methods are analyzed. On this basis, the background subtraction method based on Gaussian Mixture Model (GMM) is focused on. By describing the classic Gaussian Mixture Model and its parameter updating mathematically, point out that the classic Gaussian Mixture Model has the defects of high computational complexity and poor real-time performance. Taking into account that the shadow interference often occurs in practical application scenarios, a shadow suppression algorithm based on Gaussian Mixture Shadow Model (GMSM) in YUV (Luminance, Chrominance) color space is proposed. Proved by experiments, to some degree, this shadow suppression algorithm could eliminate some influence to the object detection accuracy caused by shadow.To solve the problems of high computational complexity, an improved fast algorithm for Gaussian Mixture Model is proposed, and experiments show that this improved algorithm is effective to enhance the efficiency of moving objects detection.(2) In terms of object tracking, the advantages and disadvantages, and applicable conditions of the tracking methods based on Filtering Theory, Partial Differential Equations and Mean Shift respectively, are analyzed. The tracking method based on Mean Shift is focused on, and the advantages such as being easy to implement and high efficiency are analyzed, as well as the disadvantages such as bad robustness caused by lack of characteristics. Against the shortcoming of the classic Mean Shift algorithm, after detailed analysis of the principle and advantages of Scale Invariant Feature Transform (SIFT) algorithm is made, a fusion algorithm combining SIFT and classic Mean Shift is proposes, followed by some experiments which prove that the fusion algorithm could effectively improve the robustness in object tracking, compared to the classic Mean Shift algorithm.(3) The application and design architecture of intelligent video surveillance systems are introduced in detail, and three subsystems, namely Pedestrian Detection and Tacking, Tripwire Detection&Regional Preparedness, and Theft Detection, are designed and implemented. Experiments and analysis to the subsystems are done, verifying that the moving object detection and tracking algorithms researched in this paper are applicable in practical applications.