Abstract
Motion Detection is major task in every application of computer vision such as video surveillance. Background Subtraction (BS) algorithms have been employed for several years to find a moving objects in a scene. BS has been used in video surveillance due to its simplicity and the fact that cameras are stationary in a video surveillance systems. The present paper introduce a new approach to motion detection through a hierarchical model that uses Mutual Information as a measure of change in the scene. Since pixels in a frame belong to objects, a segmentation in regions of the frame is done by mean-shift algorithm. Then a Mutual information measurement between the segmented region and the incoming frame is performed. A first approach to foreground mask is achieved and later is refined using a modification of the Wronskian Change Detector (WCD). The experimental results show that our proposed algorithm improve the performance in comparison with a pixel based Background Subtraction algorithm mixture of gaussians (MoG), a hierarchical block based Background Subtraction algorithm (HMDRP) and a test of linear independence (WCD).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
