Study And Application On Moving Object Detection And Tracking

Motion target detection and tracking is one of the most important subjects in computer vision,which combines advanced technologies in image processing,pattern recognition,automatic control,artificial intelligence,computer and other relative fields. It has broadly applied in military visual missile guidance, video surveillance, medical image analysis,intelligent transportation and other fields,so this subject research has important theoretical significance and practical value.This paper mainly aims to study on moving object detection and tracking, and particularly on its application in Intelligent Transportation Systems. In motion target detection, the moving objects detection algorithms usually used in stationary scene is discussed. In motion target tracking, the popular tracking methods and target tracking based on mean shift algorithm are detailed studied, then effective improved algorithms are presented considering existing problems. In the application of motion target detection and motion tracking in intelligent transportation, this paper suggests the system structure design procedure, realizes the CamShift algorithm using VisualC++6.0 and OpenCV(Open Source Computer Vision Library), and gives program code and experiment results.In motion target detection in stationary background, this paper concludes the relative technologies, introduces temporal difference, optical flow, background subtraction, and analyzed the advantages and disadvantages of every method, then studied shadow detection and removal methods.In motion target detection tracking, this paper introduces the classification of motion tracking methods and some popular tracking methods, particularly on the seizing of color features and shape features. The mean shift tracking algorithm is studied,aiming to mean shift algorithm does not use the motion information of target which may fail to track target when there are serious disturbances,an improved target tracking algorithm based on mean shift and target position prediction is proposed. According to different disturbances circumstances, adopt different scale factors to combine Kalman filter prediction result with mean shift tracking result. The improved algorithm makes good use of space position of the target,so tracking reliability is increased. Based on mean shift algorithm,continuously adaptive mean shift algorithm is then studied. This algorithm can adjust scale with object during tracking process. The searching and realization process of CamShift algorithm is also presented.In the application of intelligent transportation,compared with traditional traffic detectors, the video sensor has lots of advantages such as fast response, easy installation and maintenance, the ability to monitor wide areas and obtain more kinds of traffic parameters, and as a result, it has been widely used in Intelligent Traffic System in recent years. Up to now, a number of video processing and analyzing methods have been proposed for vehicle detection and tracking aiming at the transportation video images gained from cameras, in which the basic research area is the detection and tracking of the vehicles in transportation. This paper suggests the system structure design of moving vehicle real time detection and tracking experiment, realizes the CamShift algorithm using VisualC++6.0 and OpenCV(Open Source Computer Vision Library), and gives program code and experiment results.