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An Algorithm for Human Motion Detecting and Tracking
In this thesis, the system of intelligent monitoring pedestrian and tracking is designed. In order to improve speed and accuracy in detecting and tracking, an algorithm for human detecting and tracking is proposed. For pedestrian detection, the algorithm uses a kind of intelligent monitoring; for tracking use the Mean Shift and Cam Shift method. At first, extract the figure characters in the motion scene about model in mixed Gaussian background, then pedestrian detection based on HOG character; at last, track the result of detection based on Mean Shift and Cam Shift method. The algorithm is the key to change with the size of the target, and adaptively adjust the tracking frame to track. The experiment verified that by single pedestrians’ image, images sequence library and real-time video to test system, the system realized the people detection accuracy is higher than 100% under complicated background. It achieves a single pedestrian tracking in video and ensures pedestrian tracking box to cover objectives range greater than 95%.
Gaussian mixture background model, pedestrian detection, HOG features, pedestrian tracking.
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