Gaussian Mixture Model Based Moving Object Detection and Tracking for Traffic Surveillance
Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting the moving objects in digital videos. The moving detection and tracking system is developed based on Gaussian Mixture Model (GMM) estimation together with applicable, and combination of various relevant computer vision and image processing techniques for enhancing the process. To remove noises a median filter is used, and by applying thresholding algorithms via morphological operations the unwanted objects are removed. In addition, the object type restrictions are set using blob analysis. The results showed that the proposed system can successfully detect and track the moving objects in urban videos.
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