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SurvSurf: human retrieval on large surveillance video data

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Abstract

The volume of surveillance videos is increasing rapidly, where humans are the major objects of interest. Rapid human retrieval in surveillance videos is therefore desirable and applicable to a broad spectrum of applications. Existing big data processing tools that mainly target textual data cannot be applied directly for timely processing of large video data due to three main challenges: videos are more data-intensive than textual data; visual operations have higher computational complexity than textual operations; and traditional segmentation may damage video data’s continuous semantics. In this paper, we design SurvSurf, a human retrieval system on large surveillance video data that exploits characteristics of these data and big data processing tools. We propose using motion information contained in videos for video data segmentation. The basic data unit after segmentation is called M-clip. M-clips help remove redundant video contents and reduce data volumes. We use the MapReduce framework to process M-clips in parallel for human detection and appearance/motion feature extraction. We further accelerate vision algorithms by processing only sub-areas with significant motion vectors rather than entire frames. In addition, we design a distributed data store called V-BigTable to structuralize M-clips’ semantic information. V-BigTable enables efficient retrieval on a huge amount of M-clips. We implement the system on Hadoop and HBase. Experimental results show that our system outperforms basic solutions by one order of magnitude in computational time with satisfactory human retrieval accuracy.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, 43210, USA

    Sihao Ding, Ying Li & Yuan F. Zheng

  2. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA

    Gang Li, Xinfeng Li, Qiang Zhai, Adam C. Champion & Dong Xuan

  3. Department of Electrical and Computer Engineering, University of Macau, Macau, China

    Junda Zhu

Authors
  1. Sihao Ding
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  2. Gang Li
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  3. Ying Li
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  4. Xinfeng Li
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  5. Qiang Zhai
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  6. Adam C. Champion
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  7. Junda Zhu
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  8. Dong Xuan
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  9. Yuan F. Zheng
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Corresponding author

Correspondence to Sihao Ding.

Additional information

Sihao Ding and Gang Li are co-primary authors.

Dr. Junda Zhu’s research was supported in part by The Macau Science and Technology Development Fund under Grant FDCT 023/2013/A1, and University of Macau Research Council under Multi Year Research Grant.

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Ding, S., Li, G., Li, Y. et al. SurvSurf: human retrieval on large surveillance video data. Multimed Tools Appl 76, 6521–6549 (2017). https://doi.org/10.1007/s11042-016-3307-4

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  • DOI: https://doi.org/10.1007/s11042-016-3307-4

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