Study On Video Monitoring Of Moving Object Detection Algorithms And Implementation

Extracting and optimizing target motion from continuous videos is the key link of movement track, target recognition, video monitoring, video abstract, 3d reconstruction problems and so on. Because of its wide application and the complexity of moving object detection, it becomes one of the hot topics in the current pattern recognition and machine vision's fields.Based on Chongqing torch-plan projects named the key technology research and development of the video abstract formation and fast browsing from the embedded WEB video (CSTC2009AC2057), this study focuses on detecting moving object of dynamic scene, which is not only the core of video monitoring system, but also the foundation and the key for later processing. The accuracy of target detection decides further treatment effect of the head detection, motion tracking and video abstracting, therefore it becomes an important topic of research project. But currently popular target motion detection algorithms have difficulties in dealing with actual applications such as illumination change, background disturbance and motion objects similar to backgrounds. In the view of the above questions, main work and innovative points of this paper are as follows:Firstly the basic algorithm detecting the moving object is analyzed and implemented, and then strategies are proposed to improve the two popular background subtraction: gaussian mixture model and codebook model. For the gaussian mixture background model, gradient sequence replaces the traditional brightness sequence to calculation the probability of the model and extract clearer target outline. At the same time lookup tables of gaussian probability density distribution is established to reduce the matching time between target pixels and background model. Finally introducing spatial distribution of pixel to remove error detection and improve the accuracy of target detection. In this background model of codebook, YUV color space replaces RGB space and decrease the sensitivity to the illumination of background, while space spherical code element model in new YUV instead of cylindrical code element model in the original RGB color space improves the algorithm's robustness.Secondly, shadow cutting off, morphology processing, graph cut and regional connected technologies are adopted as posteriori optimizing operation, such as cutting off target shadow, eliminating the disturbed noises of complicated scene and purifying edge burr, thus obtains the clearer target outline providing more accurate target motion information for further treatment system.Finally, the object-oriented software method, MATLAB and vc + + are combined to design and implement platform for moving object detection module. And for various algorithm moving of targets detection, a evaluation system framework is developed, and quantitative analysis of the advantages and disadvantages of different methods are obtained. Experimental test and results prove that this paper our algorithm not only improves the accuracy of targets detection, but also have good robustness and real-time performance.