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Video Analytics for Business Intelligence

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Video Analytics for Business Intelligence

Abstract

This chapter focuses on various algorithms and techniques in video analytics that can be applied to the business intelligence domain. The goal is to provide the reader with an overview of the state of the art approaches in the field of video analytics, and also describe the various applications where these technologies can be applied. We describe existing algorithms for extraction and processing of target and scene information, multi-sensor cross camera analysis, inferencing of simple, complex and abnormal video events, data mining, image search and retrieval, intuitive UIs for efficient customer experience, and text summarization of visual data. We have also presented the evaluation results of each of these technology components using in-house and other publicly available datasets.

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References

  1. Di Battista, G., Eades, P., Tamassia, R., Tollis, I.G.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice-Hall (1999)

    Google Scholar 

  2. Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, A.: A System for Video Surveillance and Monitoring. CMU Technical Report CMU-RI-TR-00-12 (2000)

    Google Scholar 

  3. Isard, M., Blake, A.: CONDENSATION – conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)

    Article  Google Scholar 

  4. Rosales, R., Sclaroff, S.: Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling. In: IEEE Conf. On Computer Vision and Pattern Recognition, Santa Barbara, CA (1998)

    Google Scholar 

  5. Wren, C., Azarbayejani, A., Darrel, T., Pentland, A.: Pfinder: Real-time tracking of the human body. In: Proc. 2nd Int. Conf. on Automatic Face and Gesture Recognition, pp. 51–56 (1996)

    Google Scholar 

  6. Isard, M., MacCormick, J.: BraMBLe: A Bayesian multiple-blob tracker. In: Proc. ICCV (2001)

    Google Scholar 

  7. Isard, M., Blake, A.: ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893–908. Springer, Heidelberg (1998)

    Google Scholar 

  8. Viola, Jones: Robust Real-time Object Detection. In: IJCV (2001)

    Google Scholar 

  9. Kanaujia, A., Sminchisescu, C., Metaxas, D.: Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction. In: Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  10. Sminchisescu, C., Kanaujia, A., Metaxas, D.: BM3E: Discriminative Density Propagation for Visual Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 2030–2044 (2007)

    Article  Google Scholar 

  11. Gilks, W., Richardson, S., Spiegelhalter, D.: Markov Chain Monte Carlo in Practice. Chapman and Hall (1996)

    Google Scholar 

  12. Lee, M., Cohen, I.: Proposal Maps driven MCMC for Estimating Human Body Pose in Static Images. In: Proc. Computer Vision and Pattern Recognition, pp. 334–341 (2004)

    Google Scholar 

  13. Lee, M., Nevatia, R.: Dynamic Human Pose Estimation using Markov chain Monte Carlo Approach. In: MOTION, pp. 168–175 (2005)

    Google Scholar 

  14. CAESAR, Civilian American and European Surface Anthropometry Resource Project (2002)

    Google Scholar 

  15. Sun, H., Goswami, A., Metaxas, D., Bruckner, J.: Cyclogram planarity is preserved in upward slope walking. In: Proc. International Society of Biomechanics XVII Congress, Calgary, Canada (1999)

    Google Scholar 

  16. Grasso, R., Bianchi, L., Lacquaniti, F.: Motor patterns for human gait: Backward versus forward locomotion. J. Neurophysiology 80, 1868–1885 (1998)

    Google Scholar 

  17. Borghese, A., Bianchi, L., Lacquaniti, F.: Kinematic Determinants of Human Locomotion. J. Physiology (494), 863–879 (1996)

    Google Scholar 

  18. Guo, Y., et al.: Matching Vehicles under Large Pose Transformations using approximate 3D. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  19. Leotta, M.J., Mundy, J.L.: Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  20. Lou, J., Tan, T.: 3-D Model-Based Vehicle Tracking. IEEE Transactions on PAMI (2005)

    Google Scholar 

  21. Zhu, S.C., Mumford, D.B.: Quest for a stochastic grammar of images. In: Foundations and Trends of Computer Graphics and Vision (2006)

    Google Scholar 

  22. Tu, Z.W., Zhu, S.C.: Parsing images into regions, curves and curve groups. IJCV 69(2), 223–249 (2006)

    Article  Google Scholar 

  23. Babenko, B., Yang, M.H., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: CVPR (2009)

    Google Scholar 

  24. Chen, K.W., Lai, C.C., Hung, Y.P., Chen, C.S.: An adaptive learning method for target tracking across multiple cameras. In: CVPR (2008)

    Google Scholar 

  25. Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)

    Article  MATH  Google Scholar 

  26. Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: CVPR 2005 (2005)

    Google Scholar 

  27. Porikli, F.: Inter-camera color calibration by correlation model function. In: ICIP 2003 (2003)

    Google Scholar 

  28. Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS 2005 (2005)

    Google Scholar 

  29. Moore, D., Essa, I.: Recognizing Multitasked Activities using Stochastic Context-Free Grammar. In: CVPR 2001 (2001)

    Google Scholar 

  30. Earley, J.C.: An Efficient Context-Free Parsing Algorithm. PhD thesis, Carnegie-Mellon University (1968)

    Google Scholar 

  31. Jhuang, H., et al.: A Biologically Inspired System for Action Recognition. In: ICCV 2007 (2007)

    Google Scholar 

  32. Langkilde-Geary, I., Knight, K.: HALogen Input Representation, http://www.isi.edu/publications/licensed-sw/halogen/interlingua.html

  33. Knight, K.: Unification: A Multidisciplinary Survey. ACM Computing Surveys 21(1) (1989)

    Google Scholar 

  34. Pollard, C., Sag, I.A.: Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago (1994)

    Google Scholar 

  35. Rasheed, Z., Cao, X., Shafique, K., Liu, H., Yu, L., Lee, M., Ramnath, K., Choe, T., Javed, O., Haering, N.: Automated Visual Analysis in Large Scale Sensor Networks. In: ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) (September 2008)

    Google Scholar 

  36. jQuery. JavaScript library, http://www.jquery.com

  37. JSON, http://www.json.org

  38. Peres, R., Pedreira, C.: Generalized risk zone: Selecting observations for classification. IEEE PAMI 31(7) (2009)

    Google Scholar 

  39. Gray, D., Tao, H.: Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  40. Kuhn, H.W.: The Hungarian Method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)

    Article  MathSciNet  Google Scholar 

  41. Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE) 24(5), 603–619 (2002)

    Article  Google Scholar 

  42. Dukette, D., Cornish, D.: The Essential 20: Twenty Components of an Excellent Health Care Team, pp. 72–73. RoseDog Books (2009)

    Google Scholar 

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Correspondence to Asaad Hakeem .

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Hakeem, A. et al. (2012). Video Analytics for Business Intelligence. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds) Video Analytics for Business Intelligence. Studies in Computational Intelligence, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28598-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-28598-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28597-4

  • Online ISBN: 978-3-642-28598-1

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