Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences

Abstract With the increase in demand for video conferencing and IOT applications, efficient video coding standards are necessary. The performance of MPEG-4 coding scheme depends on the efficiency of the video object plane (VOP) generation methods. In head and shoulder video, such as news reading, video conferencing video sequences, the object has a very little movement in between two consecutive frames. Therefore, traditional segmentation methods could not extract the complete VOP efficiently. In this paper, we propose an efficient spatiotemporal segmentation method to extract the moving object for the generation of VOP in head and shoulder video sequences. First, a motion map of the object at each frame is generated based on the entropy of the temporal change of each pixel. Secondly, each frame is spatially segmented based on peak-means clustering approach. Finally, both motion map and spatial segmentation information are fused to extract the complete shape of the slow moving object. Experimental outcome depicts that the proposed method has highest detection accuracy with average intersection of union (IOU) score of 94.32% per frame and F1 measure of 97.75% per frame.