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Quick browsing and retrieval for surveillance videos

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Abstract

Searching for specific targets from surveillance videos requires huge workforce due to a surveillance system usually generates great amounts of video data. To alleviate the effort of human analysis, a system that helps users quickly look for targets of interest is highly demanded. In this paper, we propose a browsing and retrieval system for users to quickly locate desired targets in surveillance videos. Our basic idea is to collect all moving objects, which carry the most significant information in surveillance videos, to construct a corresponding compact video. The temporal coordinates of the moving objects in the compact video are rearranged, therefore increasing the compactness of the video. However, the appearing order of the moving objects is kept to preserve the essential activities involved in the original surveillance video. Using our system, users will spend only several minutes watching the compact video instead of hours monitoring a long surveillance video. We conducted experiments to demonstrate that the proposed system can help users quickly look for specific targets in surveillance videos.

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References

  1. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):15:1–15:58

    Article  Google Scholar 

  2. Chang S-F, Kennedy LS, Zavesky E (2007) Columbia university’s semantic video search engine. In: Proceedings of the 6th ACM international conference on image and video retrieval, CIVR

  3. Chen B-W, Wang J-C, Wang J-F (2009) A novel video summarization based on mining the story-structure and semantic relations among concept entities. IEEE Trans Multimed 11(2):295–312

    Article  Google Scholar 

  4. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  5. de Rooij O, Worring M (2010) Browsing video along multiple threads. IEEE Trans Multimed 12(2):121–130

    Article  Google Scholar 

  6. DeCamp P, Shaw G, Kubat R, Roy D (2010) An immersive system for browsing and visualizing surveillance video. In: Proceedings of ACM international conference on multimedia, ACMMM, pp 371–380

  7. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  8. Hampapur A, Brown L, Feris R, Senior A, Shu C-F, Tian Y, Zhai Y, Lu M (2007) Searching surveillance video. In: Proceedings of IEEE conference on advanced video and signal based surveillance, AVSS

  9. Hu W, Xie D, Fu Z, Zeng W, Maybank S (2007) Semantic-based surveillance video retrieval. IEEE Trans Image Process 16(4):1168–1181

    Article  MathSciNet  Google Scholar 

  10. Hu W, Xie N, Li L, Zeng X, Maybank S (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybernet C 41(6):797–819

    Article  Google Scholar 

  11. Le T-L, Boucher A, Thonnat M, Bremond F (2010) Surveillance video retrieval: what we have already done? In: Proceedings of international conference on communications and electronics, ICCE

  12. Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19

    Article  Google Scholar 

  13. Liu T, Katpelly R (2007) A two-level queueing system for interactive browsing and searching of video content. Multimed Syst 12(4–5):289–306

    Article  Google Scholar 

  14. Peng J, Xiao-Lin Q (2010) Keyframe-based video summary using visual attention clues. IEEE Trans Multimed 17(2):64–73

    Google Scholar 

  15. Pongnumkul S, Wang J, Ramos G, Cohen M (2010) Content-aware dynamic timeline for video browsing. In: Proceedings of ACM symposium on user interface software and technology, ACM UIST, pp 139–142

  16. Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990

    Article  Google Scholar 

  17. Pritch Y, Rav-Acha A, Peleg S (2008) Nonchronological video synopsis and indexing. IEEE Trans Pattern Anal Mach Intell 30(11):1971–1984

    Article  Google Scholar 

  18. Rav-Acha A, Pritch Y, Lischinski D, Peleg S (2007) Dynamosaicing: mosaicing of dynamic scenes. IEEE Trans Pattern Anal Mach Intell 29(10):1789–1801

    Article  Google Scholar 

  19. Ren K, Sarvas R, Ćalić J (2010) Interactive search and browsing interface for large-scale visual repositories. Multimed Tool Appl 49(3):513–528

    Article  Google Scholar 

  20. Schffmann K, Bailer W (2012) Video browser showdown. ACM SIGMM Records 4(1):1–2

    Article  Google Scholar 

  21. Schffmann K, Hopfgartner F, Marques O, Boeszoermenyi L, Joseb JM (2010) Video browsing interfaces and applications: a review. SPIE Rev 1

  22. The demonstration video for the compact video, https://www.youtube.com/watch?v=JDPK0zM8fdY, also available in http://nas.takming.edu.tw/pluto/research/research.html

  23. Trecvid, http://trecvid.nist.gov/

  24. Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimed Comput Commun Appl 3(1):1–37

    Article  Google Scholar 

  25. Vehicle data sets, http://nas.takming.edu.tw/pluto/research/research.html

  26. Video browser showdown, http://mmm2013.org/video_browser_showdown.htm

  27. Yong S-P, Deng JD, Purvis MK (2010) Modeling semantic context for key-frame extraction in wildlife video. In: Proceedings of 25th international conference of image and vision computing New Zealand, IVCNZ

  28. Yu X-D, Wang L, Tian Q, Xue P (2004) Multi-level video representation with application to keyframe extraction. In: Proceedings of 10th international multimedia modeling conference, MMM

  29. Yuan J, Luan H, Hou D, Zhang H, Zheng Y-T, Zha Z-J, Chua T-S (2012) Video browser showdown by nus. In: Proceedings of the international multimedia modeling conference, MMM, pp 642–645

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Correspondence to Cheng-Chieh Chiang.

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Chiang, CC., Yang, HF. Quick browsing and retrieval for surveillance videos. Multimed Tools Appl 74, 2861–2877 (2015). https://doi.org/10.1007/s11042-013-1750-z

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