Rearranging Online Tubes for Streaming Video Synopsis: A Dynamic Graph Coloring Approach.

To efficiently browse long surveillance videos, the video synopsis technique is often used to rearrange tubes ( i.e., tracks of moving objects) along the temporal axis to form a much shorter video. In this process, two key issues need to be addressed, i.e., the minimization of spatial tube collision and the maximization of temporal video condensation. In addition, when a surveillance video comes as a stream, an online algorithm with the capability of dynamically rearranging tubes is also required. Toward this end, this paper proposes a novel graph-based tube rearrangement approach for online video synopsis. The relationships among tubes are modeled with a dynamic graph, whose nodes ( i.e., object masks of tubes) and edges ( i.e., relationships) can be progressively inserted and updated. Based on this graph, we propose a dynamic graph coloring algorithm to efficiently rearrange all tubes by determining when they should appear. Extensive experimental results show that our approach can condense online surveillance video streams in real time with low tube collision and high compact ratio.