Temporal video segmentation using a switched affine models identification technique

The analysis of digital video content is of fundamental importance for efficient browsing, indexing and retrieval of video database in order to facilitate user's access to relevant data. An essential first step is the parsing of the video content into visually-coherent segments, called shots. In this paper we propose an efficient approach for shot change detection and shot modeling based on a new Switched AutoRegressive (SAR) model identification technique. We make the assumption that pixel intensities of all the frames obey a SAR model where each linear sub-model of the SAR model corresponds to a shot and each discrete state corresponds to a different event in the video. Finally, experimental results on three different video sequences show the performance and the feasibility of the proposed approach.