Research On Moving Objects Detection And Tracking For Intelligent Traffic Monitoring

Traffic video monitoring system detects the position of moving object and tracks it according to the road scene obtained by camera; and then analyses the behavior to record violations for a real-time monitoring system. However, due to the short history of development, some important problems are still unresolved, and new methods or techniques are also needed. Thus, traffic video based moving objects detection and tracking is a subject with both theoretical and practical value. This dissertation dose the researches focused on the key technical problems about intelligent traffic monitoring, and the major works include as follows:1. As the coefficients of binary redundant discrete wavelet transforms in each sub band are highly relevant, direction selectivity, and the sub-band signal is with the same size of input signal, as well as translation invariance, a redundant discrete wavelet transforms domain based motion region extraction method is presented. The moving objects are directly detected in the wavelet domain which overcomes the defects of traditional time-domain detection methods.2. For background modeling, Marr wavelet kernel function is used in a probabilistic background modeling method. Each pixel of the background image is modeled by using Marr wavelet probabilistic distribution, and the real-time input frame is used to update the background model in order to reliably deal with the interference of chaotic movement in background.3. For the shadow elimination, it is generally believed that shadow on the road has scarce details while the internal region of moving object not. A multi-scale edge geometric recognition method is used to distinguish between background shadow and foreground, and then the threshold is automatically selected for image pixel classification. The method overcomes the traditional shadow elimination methods which are influenced with the light changing, and overcomes the misjudgment if object and shadow have the similar color information, so object segmentation is more accurate.4. For vehicles tracking, based on the identification of moving objects, a SIFT (Scale Invariant Feature Transform) features of particle filter tracking technology is used to overcome the degeneration, and a detail description of object tracking technology about feature extraction and object positioning is presented. Combined with the movement and space relations, an adaptive Mean Shift filter is used to obtain the exact location of object border, and Bhattacharyya trust theory is used for the correlation coefficient to deal with the emergence and disappearance of an object. In addition, a linked list queue data association is used to record the relation between moving objects for improving the detection accuracy and reducing the complexity of computing.5. The hardware system of traffic video monitoring contains multi-camera with multi-resolution and multi-perspective, which are combined with industrial control computer system. The system can not only find vehicles of violation, but also capture the information of vehicle license by a close-range camera, which improves the efficiency of traffic monitoring and reduces the design cost.