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Key observation selection-based effective video synopsis for camera network

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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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Acknowledgments

This work was supported by 973 Program (2010CB327905) and National Natural Science Foundation of China (61272329, 61273034 and 61070104).

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Authors and Affiliations

  1. NLPR, Institute of Automation, Chinese Academic of Sciences, Beijing, 100190 , China

    Xiaobin Zhu, Jing Liu, Jinqiao Wang & Hanqing Lu

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  1. Xiaobin Zhu
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  2. Jing Liu
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  3. Jinqiao Wang
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  4. Hanqing Lu
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Correspondence to Jing Liu.

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Zhu, X., Liu, J., Wang, J. et al. Key observation selection-based effective video synopsis for camera network. Machine Vision and Applications 25, 145–157 (2014). https://doi.org/10.1007/s00138-013-0519-8

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  • DOI: https://doi.org/10.1007/s00138-013-0519-8

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