Research Of The Moving Object Detection And Tracking Based On Background Modeling

Moving object detection and tracking are the basis and prerequisite for intelligent video surveillance, and it can be applied widely in fields of transportation, military, industry and medicine. Moving object detection is a detecting process which refers to judge whether there is a foreground object in video image movement, and then locate its initial position if foreground object exists. The performance of moving object detection has a direct impact on the tracking accuracy and effectiveness of the follow-up process. Therefore, we have made a in-depth research on the moving object detection and tracking algorithm on based of background modeling.Firstly we made a study of the detection methods which object is under the situation of variable speed and light mutation. In the case of moving object is in a variable motion, we will analyze the relationship between the moving object size, speed, dwell time and the background learning rate and propose an improving measure which adopt different learning rate to update background model in different areas. When there have the light mutation and shadow areas exist in the video scene, then we use the characteristics of three-image difference which is not sensitive to the the light mutation, combining with dual-threshold segmentation method and shadow suppression model, and put forward a modified three-image difference algorithm. By the experiment, the results have showed that the improved algorithm can completely and accurately detect the contour of the moving object with the robustness.Secondly, we also made a study the traditional object tracking algorithm based on the Mean Shift algorithm. For the issue that the traditional Mean Shift algorithm is likely to cause the tracking failure in complex background environment, this paper proposes an improved tracking algorithm. The algorithm takes the centroid of the object and the LBP texture as the characteristic value. By the experiments, the results have showed that the improved tracking algorithm can effectively improve the shortcoming of missing pixel information in spatial position in traditional Mean Shift tracking algorithm, which can improve the stability and robustness of the algorithm.