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arXiv · 1608.00310

Video Summarization in a Multi-View Camera Network

Rameswar Panda·Abir Das·Amit K. Roy-Chowdhury
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Abstract

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.

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Rameswar Panda, Abir Das, Amit K. Roy-Chowdhury. 2016-08-01. Video Summarization in a Multi-View Camera Network. https://arxiv.org/abs/1608.00310

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