Summarizing video datasets in the spatiotemporal domain

We address the problem of analyzing and managing complex dynamic scenes captured in video. We present an approach to summarize video datasets by analyzing the trajectories of objects within them. Our work is based on the identification of nodes in these trajectories as critical points that describe the behavior of an object over a video segment. The time instances that correspond to these nodes are used to select critical frames for a video summary that describes adequately and concisely an object's behavior within a video segment. The analysis of relative positions of objects of interest within the video feed may dictate the selection of additional critical frames, to ensure the separability of converging trajectories. The paper presents a framework for video summarization using this approach, and addresses the use of self-organizing maps to identify trajectory nodes.