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OS-Guard: on-site signature based framework for multimedia surveillance data management

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Abstract

This paper presents OS-Guard(On-Site Guard), a novel on-site signature based framework for multimedia surveillance data management. One of the major concerns in widespread deployment of multimedia surveillance systems is the enormous amount of data collected from multiple media streams that need to be communicated, observed and stored for crime alerts and forensic analysis. This necessitates investigating efficient data management techniques to solve this problem. This work aims to tackle this problem, motivated by the following observation, more data does not mean more information. OS-Guard is a novel framework that attempts to collect informative data and filter out non-informative data on-site, thus taking a step towards solving the data management problem. In the framework, both audio and video cues are utilized by extracting features from the incoming data stream and the resultant real valued feature data is binarized for efficient storage and processing. A feature selection process based on association rule mining selects discriminant features. A short representative sample of the whole database is generated using a novel reservoir sampling algorithm that is stored onsite and used with an support vector machine to classify an important event. Initial experiments for a Bank ATM monitoring scenario demonstrates promising results.

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References

  1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: ACM SIGMOD international conference on management of data, pp 207–216

  2. Ali W, Alimi AM (2010) Incremental learning approach for events detection from large video dataset. Seventh IEEE international conference on advanced video and signal based surveillance, pp 555–560

  3. Bobick AF, Davis JW (2001) The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 23(3):257–267

    Article  Google Scholar 

  4. Boiman O, Irani M (2005) Detecting irregularities in images and in video. In: Proceedings IEEE international conference on computer vision, pp 1985–1988

  5. Borgelt C (2010) Apriori-association rule induction. http://www.borgelt.net/apriori.html. Accessed 23 December 2010

  6. Bradski G, Davis J (2002) Motion segmentation and pose recognition with motion history gradient. International Journal of Machine Vision and Applications 13(2):174–184

    Article  Google Scholar 

  7. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 23 December 2010

  8. Chen M, Chen S-C, Shyu M-L, Wickramaratna K (2006) Semantic event detection via multimodal data mining. IEEE Signal Process Mag 23:38–46

    Article  Google Scholar 

  9. Collins RT, Lipton AJ, Kanade T, Fujiyoshi H, Duggins D, Sin YT, Tolliver D, Enomoto N, Hasegawa O, Burt P, Wixson L (2000) VSAM: a system for video surveillance and monitoring. PA, Technical Report CMU-RI-TR-00-12, Carnegie Mellon Univ., Pittsburgh

  10. Cowling M, Sitte R (2003) Comparison of techniques for environmental sound recognition. Pattern Recogn Lett 24(15):2895–2907

    Article  Google Scholar 

  11. Fayolle J, Ducottet C, Schon JP (1998) Application of multiscale characterization of edges to motion determination. IEEE Trans Signal Process 46(4):1174–1179

    Article  Google Scholar 

  12. Ganchev T, Fakotakis N, Kokkinakis G (2005) Comparative evaluation of various mfcc implementations on the speaker verification task. In: 10th international conference on speech and computer (SPECOM 2005), vol 1, pp 191–194

  13. Gonzalez R (2007) Enhancing video surveillance with audio events. 9th Biennial conference of the Australian pattern recognition society on digital image computing techniques and applications, pp 61–66

  14. Haritaoglu I, Harwood D, Davis LS (2000) W4: real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22:809–830

    Article  Google Scholar 

  15. Hu M-K (1962) Visual pattern recognition by moment invariants. IEEE Trans Inf Theory 8(2):179–187

    Article  MATH  Google Scholar 

  16. Kieran D, Yan WQ (2010) A framework for an event driven video surveillance system. Seventh IEEE international conference on advanced video and signal based surveillance, pp 97–102

  17. Leo M, Spagnolo P, D’Orazio T, Distante A (2004) Human activity recognition in archaeological sites by hidden Markov models. PCM 2:1019–1026

    Google Scholar 

  18. Marple S (1987) Digital spectral analysis, 2 edn. Prentice-Hall

  19. Mittal A, Cheong LF (2003) Framework for synthesizing semantic-level indexes. Multimed Tools Appl 20(2):135–158

    Article  Google Scholar 

  20. Niu W, Jiao L, Han D, Wang Y (2003) Real-time multi-person tracking in video surveillance. In: Proceedings of the Pacific Rim multimedia conference vol 2, pp 1144–1148

  21. Oh J, Bandi B (2002) Multimedia data mining framework for raw video sequence. In: Proceedings of internatonal workshop multimedia data management, pp 1–10

  22. Radhakrishnan R, Divakaran A, Smaragdis A (2005) Audio analysis for surveillance applications. IEEE workshop on applications of signal processing to audio and acoustics, pp 158– 161

  23. Spackman KA (1989) Signal detection theory: valuable tools for evaluating inductive learning. In: Proceedings of the sixth international workshop on machine learning, pp 160–163

  24. Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: Proceedings IEEE conf. computer vision and pattern recognition, vol 2, pp 246–252

  25. Stringa E, Regazzoni C (2000) Real time video shot detection for surveillance applications. IEEE Trans Image Process 9:69–79

    Article  Google Scholar 

  26. Tax DMJ, Laskov P (2003) Online svm learning: from classification and data description and back. In: Proceedings neural networks and signal processing, pp 499–508

  27. Tziakos I, Cavallaro A, Xu LQ (2010) Local abnormality detection in video using subspace learning. In: Seventh IEEE international conference on advanced video and signal based surveillance, pp 519–525

  28. Valera M, Velastin SA (2005) Intelligent distributed surveillance systems: a review. In: IEE proceedings image and signal processing, vol 152, pp 192–204

  29. Wijesekera D, Barbara D (2000) Mining cinematic knowledge. In: Proceedings of international workshop on multimedia data mining, pp 98–103

  30. Xiong Z, Radhakrishnan R, Divakaran A, Huang TS (2003) Comparing mfcc and mpeg-7 audio features for feature extraction, maximum likelihood hmm and entropic prior hmm for sports audio classification, pp 397–400

  31. Zhang D, Gatica-Perez D, Bengio S, McCowan I (2005) Semi-supervised adapted hmms for unusual event detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, vol 1, pp 611–618

  32. Zhu X, Wu X, Elmagarmid AK, Feng Z, Wu L (2005) Video data mining: semantic indexing and event detection from the association perspective. IEEE Trans Knowl Data Eng 17(5):665–677

    Article  Google Scholar 

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Authors and Affiliations

  1. Gokaraju Rangaraju Institute of Engineering College, Hyderabad, India

    Praveen Kumar

  2. Institute for Infocomm Research, 1 Fusionopolis way #21-01, Connexis (South Tower), 138632, Singapore

    Sujoy Roy

  3. College of Engineering, Roorkee, India

    Ankush Mittal

Authors
  1. Praveen Kumar
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  2. Sujoy Roy
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  3. Ankush Mittal
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Correspondence to Praveen Kumar.

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Kumar, P., Roy, S. & Mittal, A. OS-Guard: on-site signature based framework for multimedia surveillance data management. Multimed Tools Appl 59, 363–382 (2012). https://doi.org/10.1007/s11042-010-0693-x

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