Pedestrian Tracking Algorithm In Surveillance Video

Human detection and pedestrian tracking in video belong to the category of computer vision,and it is also a hot topic in recent years,and is also widely used in our lives,such as intelligent traffic control,public security,intelligent monitoring and so on.Researching related algorithms is of great significance for the application of intelligent transportation and video surveillance.In order to provide accurate real-time monitoring and tracking of pedestrians in real-time monitoring video,this paper studies the three parts of the pedestrian tracking system.The first part is the detection of moving targets,the second part is human detection,and the last part is the tracking algorithm,which is optimized and improved for some algorithms.The main research contents and contributions of this paper can be divided into the following aspects:1?In the aspect of moving target detection.The advantages and disadvantages of several mainstream algorithms are deeply understood and analyzed.They are mixed gaussian model,multi-model mean model and VIBE model.The multi-model mean value model is adopted as the background model to set adaptive thresholds,and different thresholds are adopted in different regions to reduce the noise.In the detection,background difference method is used first,and the next inter-frame difference method is used if the anomaly is detected.If the anomaly is not detected,no subsequent detection is required.By detecting the result of the background difference method by the interframe difference method,the missed detection rate and the false alarm rate can be reduced,and the detection accuracy can be improved.2.In terms of physical examination.Because the head and shoulder contour has good representativeness and is relatively stable for detection,the head and shoulder contour of a person should be selected as the area to be tested before detection.Next,the human body features are extracted,and the illuminance invariant color feature is selected as the first feature of human body detection.The shortcomings of the LBP texture feature are improved,the type of the optimized binary mode is greatly reduced,and no information is missing,which is the second feature of human body detection.The fusion of the two features is fused using the BWH fusion algorithm.The histogram cross-core support vector machine SVM is optimized and used as a classifier for classification,which can reduce the computational complexity.3.Pedestrian tracking.KCF algorithm is adopted to track pedestrians.Its disadvantage is that the tracking target frame is preset according to the size of the target in the first frame.In the process,the moving target may undergo size deformation,but the target frame will not change with the change of the moving target,which may lead to the tracking target failure.Inthis paper,multi-scale tracking is adopted for pedestrians,and the initial target is scaled to find the optimal scale value of the tracking target,which overcomes the tracking failure problem caused by the target frame not changing with the change of the target and improves its accuracy.In this paper,a human detection and pedestrian tracking algorithm is designed,and the simulation experiment on the actual scene shows that it has good accuracy and real-time.