Context-Aware Surveillance Video Summarization.

We present a method that is able to find the most informative video portions, leading to a summarization of video sequences. In contrast to the existing works, our method is able to capture the important video portions through information about individual local motion regions, as well as the interactions between these motion regions. Specifically, our proposed Context-Aware Video Summarization (CAVS) framework adopts the methodology of sparse coding with generalized sparse group lasso to learn a dictionary of video features and a dictionary of spatio-temporal feature correlation graphs. Sparsity ensures that the most informative features and relationships are retained. The feature correlations, represented by a dictionary of graphs, indicate how motion regions correlate to each other globally. When a new video segment is processed by CAVS, both dictionaries are updated in an online fashion. Specifically, CAVS scans through every video segment to determine if the new features along with the feature correlations, can be sparsely represented by the learned dictionaries. If not, the dictionaries are updated, and the corresponding video segments are incorporated into the summarized video. The results on four public datasets, mostly composed of surveillance videos and a small amount of other online videos, show the effectiveness of our proposed method.