Video Summarization in a Multi-View Camera Network
This addresses the problem of summarizing videos from multiple camera angles, which is incremental over single-view methods.
The paper tackles video summarization for multi-view camera networks by proposing a framework that exploits intra- and inter-view correlations in a joint embedding space, achieving state-of-the-art performance on benchmark datasets.
While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space. We learn the embedding by minimizing an objective function that has two terms: one due to intra-view correlations and another due to inter-view correlations across the multiple views. The solution can be obtained directly by solving one Eigen-value problem that is linear in the number of multi-view videos. We then employ a sparse representative selection approach over the learned embedding space to summarize the multi-view videos. Experimental results on several benchmark datasets demonstrate that our proposed approach clearly outperforms the state-of-the-art.