Video summarization through change detection in a non-overlapping camera network
We present a method that is able to find the most informative video portions in a non-overlapping camera network, leading to a summarization of the multiple video sequences. This is posed as a problem of detecting changes in the interactions between the targets in the network of cameras. Examples include formation and dispersal of groups within the view of a single camera, as well as identifying changes between cameras. The latter includes prediction of events that may have occurred in the gaps between the cameras. The solution strategy is built upon a social group identification method and a track association strategy, which together are used to indicate conflicts in the interactions between the targets, leading to identification of the most informative video portions in a non-overlapping camera network. We apply our algorithm on a public dataset with multiple non-overlapping cameras on a university campus. We show examples of informative video segments, as well as perform a statistical analysis of the results.