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Machine Vision and Applications

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01-01-2014 | Special Issue Paper

Key observation selection-based effective video synopsis for camera network

Authors: Xiaobin Zhu, Jing Liu, Jinqiao Wang, Hanqing Lu

Published in: Machine Vision and Applications | Issue 1/2014

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Abstract

Nowadays, tremendous amount of video is captured endlessly from increased numbers of video cameras distributed around the world. Since needless information is abundant in the raw videos, making video browsing and retrieval is inefficient and time consuming. Video synopsis is an effective way to browse and index such video, by producing a short video representation, while keeping the essential activities of the original video. However, video synopsis for single camera is limited in its view scope, while understanding and monitoring overall activity for large scenarios is valuable and demanding. To solve the above issues, we propose a novel video synopsis algorithm for partially overlapping camera network. Our main contributions reside in three aspects: First, our algorithm can generate video synopsis for large scenarios, which can facilitate understanding overall activities. Second, for generating overall activity, we adopt a novel unsupervised graph matching algorithm to associate trajectories across cameras. Third, a novel multiple kernel similarity is adopted in selecting key observations for eliminating content redundancy in video synopsis. We have demonstrated the effectiveness of our approach on real surveillance videos captured by our camera network.

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Title: Key observation selection-based effective video synopsis for camera network
Authors: Xiaobin Zhu
Jing Liu
Jinqiao Wang
Hanqing Lu
Publication date: 01-01-2014
Publisher: Springer Berlin Heidelberg
Published in: Machine Vision and Applications / Issue 1/2014
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-013-0519-8

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