Motion-State-Adaptive Video Summarization via Spatiotemporal Analysis

With the explosive growth of video data, managing and browsing videos in a timely and effective manner has become an urgent problem, particularly in surveillance applications. Video summarization as a feasible solution is considerably attracting more attention. In this paper, we propose a novel motion-state-Adaptive video summarization method based on spatiotemporal analysis. To overcome the low efficiency of traditional video summarization, the proposed method utilizes spatiotemporal slices to analyze object motion trajectories and selects motion state changes as a metric to summarize videos. Initially, a motion-Active segment is detected using motion power. Subsequently, motion state changes are modeled as a collinear segment on a spatiotemporal slice (STS-CS) and an attention curve based on the STS-CS model is formed to extract the key frames. Finally, a visually distinguishing mechanism is employed to refine the key frames. The experimental results demonstrate that the proposed method outperforms the existing state-of-The-Art methods in terms of both computational efficiency and detailed video motion dynamic maintenance. This is accomplished with a comparable subjective performance.