Multi-View Egocentric Video Summarization
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With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content. Nonetheless, the spread of social and egocentric cameras tends to create an abundance of sparse scenarios captured by several devices, and ultimately required to be jointly summarized. In this paper, we propose the problem of summarizing videos recorded simultaneously by several egocentric cameras that intermittently share the field of view. We present a supervised-learning framework that (a) identifies a diverse set of important events among dynamically moving cameras that often are not capturing the same scene, and (b) selects the most representative view(s) at each event to be included in the universal summary. A key contribution of our work is collecting a new multi-view egocentric dataset, Multi-Ego, due to the lack of an applicable and relevant alternative. Our dataset consists of 41 sequences, each recorded simultaneously by 3 cameras and covering a wide variety of real-life scenarios. The footage is annotated comprehensively by multiple individuals under various summarization settings: (a) single view, (b) two view, and (c) three view, with a consensus analysis ensuring a reliable ground truth. We conduct extensive experiments on the compiled dataset to show the effectiveness of our approach over several state-of-the-art baselines. We also show that it can learn from data of varied number-of-views, deeming it a scalable and a generic summarization approach. Our dataset and materials are publicly available.
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