Video object segmentation for head–shoulder sequences in the cellular neural networks architecture

Abstract

MPEG-4 introduces the concept of video object to support content-based functionalities. Video object segmentation is a key step in defining the contents of any video sequences. Head–shoulder sequence (HSS) is typical in video conferencing and surveillance systems, in which real-time performance is required. Since background information can be obtained in advance and pre-stored, video segmentation for HSS can use background information a priori. To avoid the critical selection of threshold for gradient-based method, and to overcome the insufficiency of monochrome intensity-based change detection, an efficient color edge-based change detection scheme (CECD) is utilized in this paper. In order to meet the real-time performance for HSS, it is implemented in the cellular neural networks (CNN) architecture. The algorithm is mainly based on 3 by 3, linear templates. Because of CNN's high parallelism and computational abilities, real-time performance is achieved. Experimental results on several test sequences show the robustness of this approach. It can achieve better spatial accuracy and temporal coherency than COST211 AM.