Video Summarization in a Multi-View Camera Network

TL;DR

This paper introduces a novel multi-view video summarization framework that leverages intra- and inter-view correlations in a joint embedding space, improving summarization quality over existing methods.

Contribution

It proposes a new joint embedding approach for multi-view videos that captures correlations across views and employs sparse selection for summarization.

Findings

Outperforms state-of-the-art on benchmark datasets

Efficient eigenvalue-based solution for embedding learning

Effective multi-view video summarization results

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.