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 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 to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms via morphological operations. In addition, the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.
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