A Novel approach to Detect and Track Moving Object using Partitioning and Normalized Cross Correlation

Research in motion analysis has evolved over the years as a challenging field, such as traffic monitoring, military, medicine and biological sciences etc. Detection and tracking of moving objects in video sequences can offer significant benefits to motion analysis. In this an approach is proposed for the detection and tracking of moving object in an image sequence. Two consecutive frames from image sequence are partitioned into four quadrants and then the Normalized Cross Correlation (NCC) is applied to each sub frame. The sub frame which has minimum value of NCC, indicates the presence of moving object. Next step is to identify the location of the moving object. Location of the moving object is obtained by performing component connected analysis and morphological processing. After that the centroid calculation is used to track the moving object. Number of experiments performed using indoor and outdoor image sequences. The results are compared with Simple Difference (SD) and Background Subtraction (BS) methods. The proposed algorithm gives better performance in terms of Detection Rate (DR) and processing time per frame.