Selecting attentive frames from visually coherent video chunks for surveillance video summarization
Wenzhong Wang, Qiaoqiao Zhang, Bin Luo, Jin Tang, Rui Ruan, Chenglong Li · 2017
This paper investigates how to extract key-frames from surveillance video while maximizing their diversity and representational ability. We solve this problem by two steps, i.e., video partition and frame selection. The first step is to partition a surveillance video into visually coherent video chunks, which have high intra-chunk similarity and interchunk dissimilarity. In particular, we propose an object-based frame metric to measure the relevance of two frames, and apply the Normalized Cut algorithm to achieve video partition. The second step is to select the attentive frames from the partitioned video chunks. We propose an attention score based on the content completeness and the visual satisfaction for each frame, and select most attentive frame with highest attention score in each chunk. Extensive experiments on both public and our newly created datasets suggest that our approach significantly outperforms other video summarization methods.