The Visual Computer

, Volume 29, Issue 10, pp 983–1009 | Cite as

A survey on activity recognition and behavior understanding in video surveillance

Original Article

Abstract

This paper provides a comprehensive survey for activity recognition in video surveillance. It starts with a description of simple and complex human activity, and various applications. The applications of activity recognition are manifold, ranging from visual surveillance through content based retrieval to human computer interaction. The organization of this paper covers all aspects of the general framework of human activity recognition. Then it summarizes and categorizes recent-published research progresses under a general framework. Finally, this paper also provides an overview of benchmark databases for activity recognition, the market analysis of video surveillance, and future directions to work on for this application.

Keywords

Image processing Automated surveillance Video tracking Human activity 

References

  1. 1.
    Aggarwal, J.K., Cai, Q.: Human motion analysis: a review. Comput. Vis. Image Underst. 73(3), 428–440 (1999) Google Scholar
  2. 2.
    Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43(3), 1–43 (2011) Google Scholar
  3. 3.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 06, 37–66 (1991) Google Scholar
  4. 4.
    Allili, M.S., Bouguila, N., Ziou, D.: A robust video foreground segmentation by using generalized Gaussian mixture modeling. In: 4th Canadian Conf. on Computer and Robot Vision, pp. 503–509 (2007) Google Scholar
  5. 5.
    Bayona, A., SanMiguel, J.C., Martínez, J.M.: Stationary foreground detection using background subtraction and temporal difference in video surveillance. In: IEEE 17th Int. Conf. on Image Processing, pp. 1–4 (2010) Google Scholar
  6. 6.
    Blunsom, P.: Hidden Markov models. Tech. rep, Human Language Technology University of Melbourne, Victoria, Australia (2004). http://www.cs.mu.oz.au/460/2004/materials/hmm-tutorial.pdf
  7. 7.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001) Google Scholar
  8. 8.
    Bobick, A.F., Wilson, A.D.: A state-based approach to the representation and recognition of gesture. IEEE Trans. Pattern Anal. Mach. Intell. 19(12), 1325–1337 (1997) Google Scholar
  9. 9.
    Bose, B., Grimson, E.: Improving object classification in far-field video. In: Proc. of the Int. Conf. on Computer Vision and Pattern Recognition, pp. 181–188. IEEE Computer Society, Washington (2004) Google Scholar
  10. 10.
    Brown, L.M.: View independent vehicle/person classification. In: Proc. of the ACM 2nd Int. Workshop on Video Surveillance & Sensor Networks, pp. 114–123. ACM Press, New York (2004) Google Scholar
  11. 11.
    Bucak, S.S., Gunsel, B., Gursoy, O.: Incremental nonnegative matrix factorization for background modeling in surveillance video. In: IEEE 15th Signal Processing and Communications Applications (SIU), pp. 1–4 (2007) Google Scholar
  12. 12.
    Cai, L., He, L., Yamashita, T., Xu, Y., Zhao, Y., Yang, X.: Robust contour tracking by combining region and boundary information. IEEE Trans. Circuits Syst. Video Technol. 21(12), 1784–1794 (2011) Google Scholar
  13. 13.
    Campbell, L., Bobick, A.: Recognition of human body motion using phase space constraints. In: ICCV, pp. 624–630 (1995) Google Scholar
  14. 14.
    Camplani, M., Salgado, L.: Adaptive background modeling in multicamera system for real-time object detection. Opt. Eng. 50(12), 1–17 (2011) Google Scholar
  15. 15.
    Cavallaro, A., Steiger, O., Ebrahimi, T.: Tracking video objects in cluttered background. IEEE Trans. Circuits Syst. Video Technol. 15(4), 575–584 (2005) Google Scholar
  16. 16.
    Chai, Y., Shin, S., Chang, K., Kim, T.: Real-time user interface using particle filter with integral histogram. IEEE Trans. Consum. Electron. 56(2), 510–515 (2010) Google Scholar
  17. 17.
    Chang, S.F.: The holy grail of content-based media analysis. IEEE Multimed. 9(2), 6–10 (2002) Google Scholar
  18. 18.
    Chen, L., Yang, H., Takaki, T., Ishii, I.: Real-time frame-straddling-based optical flow detection. In: Proc. of IEEE Int. Conf. on Robotics and Biomimetics, pp. 2447–2452 (2011) Google Scholar
  19. 19.
    Chen, Q., Sun, Q.S., Heng, P.A., Xia, D.S.: Two-stage object tracking method based on kernel and active contour. IEEE Trans. Circuits Syst. Video Technol. 20(4), 605–609 (2010) Google Scholar
  20. 20.
    Chen, Y., Zhang, L., Lin, B., Xu, Y., Ren, X.: Fighting detection based on optical flow context histogram. In: Proc. of IEEE 2nd Int. Conf. on Innovations in Bio-inspired Computing and Applications, pp. 95–98 (2011) Google Scholar
  21. 21.
    Cheng, F.H., Chen, Y.L.: Real time multiple objects tracking and identification based on discretewavelet transform. Pattern Recognit. 39, 1126–1139 (2006) MathSciNetMATHGoogle Scholar
  22. 22.
    Cheung, K., Baker, S., Kanade, T.: Shape-from-silhouette across time part II: applications to human modeling and markerless motion tracking. Int. J. Comput. Vis. 63(3), 225–245 (2005) Google Scholar
  23. 23.
    Chiverton, J., Mirmehdi, M., Xie, X.: On-line learning of shape information for object segmentation and tracking. In: Proc. of British Machine Vision Conference, pp. 1–11 (2009) Google Scholar
  24. 24.
    Chiverton, J., Xie, X., Mirmehdi, M.: Automatic bootstrapping and tracking of object contours. IEEE Trans. Image Process. 21(3), 1231–1245 (2012) MathSciNetGoogle Scholar
  25. 25.
    Chomat, O., Crowley, J.L.: Probabilistic recognition of activity using local appearance. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 637–663 (1999) Google Scholar
  26. 26.
    Cohen, C.J., Morelli, F., Scott, K.A.: A surveillance system for recognition of intent within individuals and crowds. In: Conf. on Technologies for Homeland Security, Waltham, MA, pp. 559–565. IEEE Press, New York (2008) Google Scholar
  27. 27.
    Cohen, W.W.: Fast effective rule induction. In: Proc. of 12th Int. Conf. on Machine Learning, pp. 115–123. Morgan Kaufmann, San Mateo (1995) Google Scholar
  28. 28.
    Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res., Part C, Emerg. Technol. 6(4), 271–288 (1998) Google Scholar
  29. 29.
    Collins, R.T., Lipton, A.J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A system for video surveillance and monitoring. Tech. rep, Robotics Institute at Carnegie Mellon University (2000) Google Scholar
  30. 30.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003) Google Scholar
  31. 31.
    Cupillard, F., Bremond, F., Thonnat, M.: Group behavior recognition with multiple cameras. In: Proc. 6th IEEE Workshop on Applications of Computer Vision, pp. 177–183 (2002) Google Scholar
  32. 32.
    Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 781–796 (2000) Google Scholar
  33. 33.
    Dai, P., Di, H., Dong, L., Tao, L., Xu, G.: Group interaction analysis in dynamic context. IEEE Trans. Syst. Man Cybern. 38(1), 275–282 (2008) Google Scholar
  34. 34.
    Damen, D., Hogg, D.: Recognizing linked events: searching the space of feasible explanations. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 927–934 (2009) Google Scholar
  35. 35.
    Darrell, T., Pentland, A.: Space-time gestures. In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 335–340 (1993) Google Scholar
  36. 36.
    Denman, S., Chandran, V., Sridharan, S.: Adaptive optical flow for person tracking. In: Proc. of the Digital Imaging Computing: Techniques and Applications, DICTA ’05, pp. 1–7 (2005) Google Scholar
  37. 37.
    Denman, S., Chandran, V., Sridharan, S.: An adaptive optical flow technique for person tracking systems. Pattern Recognit. Lett. 28(10), 1232–1239 (2007) Google Scholar
  38. 38.
    Denman, S., Fookes, C., Sridharan, S.: Improved simultaneous computation of motion detection and optical flow for object tracking. In: IEEE Digital Image Computing: Techniques and Applications, pp. 175–182 (2009) Google Scholar
  39. 39.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Int. Conf. on Computer Communications and Networks, vol. 14, pp. 65–72. IEEE Press, New York (2005) Google Scholar
  40. 40.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, Stanford Research Institute, Menlo Park (1973) MATHGoogle Scholar
  41. 41.
    Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: Proc. 9th IEEE Int. Conf. on Computer Vision, vol. 2, pp. 726–733 (2003) Google Scholar
  42. 42.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Frame-Rate Workshop, pp. 751–767. IEEE Press, New York (2000) Google Scholar
  43. 43.
    Fazli, S., Pour, H.M., Bouzari, H.: Multiple object tracking using improved GMM based motion segmentation. In: IEEE ECTI-CON, vol. 2, pp. 1130–1133 (2009) Google Scholar
  44. 44.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (2003) Google Scholar
  45. 45.
    Filipovych, R., Ribeiro, E.: Combining models of pose and dynamics for human motion recognition. In: 3rd International Springer Symposium on Advances in Visual Computing, Aberdeen, Scotland, pp. 21–32 (2007) Google Scholar
  46. 46.
    Forsyth, D.A., Arikan, O., Ikemoto, L., O’Brien, J., Ramanan, D.: Computational studies of human motion: part 1, tracking and motion synthesis. Found. Trends Comput. Graph. Vis. 1(02/03), 77–254 (2005) Google Scholar
  47. 47.
    Gallagher, M., Downs, T.: Visualization of learning in multilayer perceptron networks using principal component analysis. IEEE Trans. Syst. Man Cybern. 33, 28–34 (2003) Google Scholar
  48. 48.
    Gavrilla, D., Davis, L.: 3D Model-based tracking of humans in action: a multi-view approach. In: Int. Proc. of the Computer Vision and Pattern Recognition, pp. 73–80 (1996) Google Scholar
  49. 49.
    Ghanem, N., DeMenthon, D., Doermann, D., Davis, L.: Representation and recognition of events in surveillance video using Petri nets. In: Conf. on Computer Vision and Pattern Recognition Workshop, pp. 112–121 (2004) Google Scholar
  50. 50.
    Gilbert, A., Illingworth, J., Bowden, R.: Fast realistic multi-action recognition using mined dense spatio-temporal features. In: IEEE 12th Int. Conf. on Computer Vision, pp. 925–931 (2009) Google Scholar
  51. 51.
    Girisha, R., Murali, S.: Tracking humans using novel optical flow algorithm for surveillance videos. In: Proceedings of the 4th Annual ACM Bangalore Conf., COMPUTE ’11, pp. 1–8 (2011) Google Scholar
  52. 52.
    Gong, S., Xiang, T.: Recognition of group activities using dynamic probabilistic networks. In: Proc. 9th IEEE Int. Conf. on Computer Vision, vol. 2, pp. 742–749 (2003) Google Scholar
  53. 53.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007) Google Scholar
  54. 54.
    Gupta, A., Davis, L.S.: Objects in action: an approach for combining action understanding and object perception. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007) Google Scholar
  55. 55.
    Gupta, A., Srinivasan, P., Shi, J., Davis, L.S.: Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2012–2019 (2009) Google Scholar
  56. 56.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W 4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 309–330 (2000) Google Scholar
  57. 57.
    Heisele, B., Ho, P., Wu, J., Poggio, T.: Face recognition: component-based versus global approaches. Comput. Vis. Image Underst. 91, 6–21 (2003) Google Scholar
  58. 58.
    Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. Int. J. Comput. Vis. 80, 3–15 (2008) Google Scholar
  59. 59.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern., Part C, Appl. Rev. 34(3), 334–352 (2004) Google Scholar
  60. 60.
    Hu, W., Xie, D., Tan, T., Maybank, S.: Learning activity patterns using fuzzy self-organizing neural network. IEEE Trans. Syst. Man Cybern. 34(3), 1618–1626 (2004) Google Scholar
  61. 61.
    Huang, J., et al.: GPU-accelerated computation for robust motion tracking using the CUDA framework. In: Int. Conf. on Visual Information Engineering, vol. 5, pp. 437–442 (2008) Google Scholar
  62. 62.
    11th IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance (2009). http://www.cvg.rdg.ac.uk/PETS2009/authors.html
  63. 63.
    Imagery Library for Intelligent Detection Systems (2010). http://www.ilids.co.uk
  64. 64.
  65. 65.
    Ince, S., Konrad, J.: Occlusion-aware optical flow estimation. IEEE Trans. Image Process. 17(8), 1443–1451 (2008) MathSciNetGoogle Scholar
  66. 66.
    Intille, S.S., Bobick, A.F.: A framework for recognizing multi-agent action from visual evidence. In: AAAI-99, pp. 518–525. AAAI Press, Menlo Park (1999) Google Scholar
  67. 67.
    Ishii, I., Taniguchi, T., Yamamoto, K., Takaki, T.: 1000 fps real-time optical flow detection system. Proc. SPIE 7538, 1–11 (2010) Google Scholar
  68. 68.
    Ivanov, Y.A., Bobick, A.F.: Recognition of visual activities and interactions by stochastic parsing. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 852–872 (2000) Google Scholar
  69. 69.
    Jan, T.: Neural network based threat assessment for automated visual surveillance. In: Int. Joint Conf. on Neural Networks, vol. 2, pp. 1309–1312. IEEE Press, New York (2004) Google Scholar
  70. 70.
    Jang, D.S., Choi, H.I.: Active models for tracking moving objects. Pattern Recognit. 33(7), 1135–1146 (2000) Google Scholar
  71. 71.
    Javed, O., Shah, M.: Tracking and object classification for automated surveillance. In: Proc. of the 7th European Conference on Computer Vision, pp. 343–357. Springer, London (2002) Google Scholar
  72. 72.
    Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognit. 44, 2231–2240 (2011) Google Scholar
  73. 73.
    Jiang, H., Drew, M.S., Li, Z.N.: Successive convex matching for action detection. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 1646–1653 (2006) Google Scholar
  74. 74.
    Joo, S.W., Chellappa, R.: Attribute grammar-based event recognition and anomaly detection. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW ’06, pp. 107–114 (2006) Google Scholar
  75. 75.
    Kameda, Y., Minoh, M.: A human motion estimation method using 3-successive video frames. In: Proc. of Int. Conf. on Virtual Systems, pp. 135–140 (1996) Google Scholar
  76. 76.
    Kang, W., Deng, F.: Research on intelligent visual surveillance for public security. In: 6th Int. Conf. Comput. and Inf. Sci, pp. 824–829. IEEE/ACIS, Melbourne (2007) Google Scholar
  77. 77.
    Ke, Y., Sukthankar, R., Hebert, M.: Spatio-temporal shape and flow correlation for action recognition. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007) Google Scholar
  78. 78.
    Khan, S.M., Shah, M.: Detecting group activities using rigidity of formation. In: Proc. of the 13th Annual ACM Int. Conf. on Multimedia, pp. 403–406 (2005) Google Scholar
  79. 79.
    Kim, H., Sakamoto, R., Kitahara, I., Toriyama, T., Kogure, K.: Robust silhouette extraction technique using background subtraction. In: 10th Meeting on Image Recognition and Understand (MIRU), Hiroshima, Japan, pp. 1–6 (2007) Google Scholar
  80. 80.
    Kim, J.B., Kim, H.J.: Efficient region-based motion segmentation for a video monitoring system. Pattern Recognit. Lett. 24(1/3), 113–128 (2003) Google Scholar
  81. 81.
    Kim, T.K., Im, J.H., Paik, J.K.: Video object segmentation and its salient motion detection using adaptive background generation. IEEE Power Electron. Lett. 45(11), 542–543 (2009) Google Scholar
  82. 82.
    Ko, T.: A survey on behavior analysis in video surveillance for homeland security applications. In: AIPR, pp. 1–8. IEEE Press, New York (2008) Google Scholar
  83. 83.
    Kuno, Y., Watanabe, T., Shimosakoda, Y., Nakagawa, S.: Automated detection of human for visual surveillance system. In: Proc. of the Int. Conf. on Pattern Recognition, ICPR ’96, pp. 865–869. IEEE Computer Society, Washington (1996) Google Scholar
  84. 84.
    Ladikos, A., Benhimane, S., Navab, N.: A realtime tracking system combining template-based and feature-based approaches. In: VISAPP (2007) Google Scholar
  85. 85.
    Lalos, C., Anagnostopoulos, V.: Hybrid tracking approach for assistive environments. In: In Int. Conf. Proc. Series, 05, vol. 39/64. ACM Press, New York (2009) Google Scholar
  86. 86.
    Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005) Google Scholar
  87. 87.
    Laptev, I., Lindeberg, T.: Space-time interest points. In: Proc. 9th IEEE Int. Conf. on Computer Vision, pp. 432–439 (2003) Google Scholar
  88. 88.
    Laptev, I., Perez, P.: Retrieving actions in movies. In: Proc. of the 11th IEEE Int. Conf. on Computer Vision, pp. 1–8 (2007) Google Scholar
  89. 89.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008) Google Scholar
  90. 90.
    Leordeanu, M., Collins, R.: Unsupervised learning of object features from video sequences. In: Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Washington, DC, USA, vol. 1, pp. 1142–1149 (2005) Google Scholar
  91. 91.
    Li, X., Hu, W., Zhang, Z., Zhang, X.: Robust foreground segmentation based on two effective background models. In: Proc. of the 1st ACM Int. Conf. on Multimedia Information Retrieval, MIR ’08, pp. 223–228. ACM Press, New York (2008) Google Scholar
  92. 92.
    Liao, H.H., Chang, J.Y., Chen, L.G.: A localized approach to abandoned luggage detection with foreground-mask sampling. In: Proc. of the IEEE 5th Int. Conf. on Advanced Video and Signal Based Surveillance, AVSS’08, pp. 132–139. IEEE Computer Society, Washington (2008) Google Scholar
  93. 93.
    Lin, F., Chen, B.M., Lee, T.H.: Robust vision-based target tracking control system for an unmanned helicopter using feature fusion. In: 9th IAPR Int. Conf. on Machine Vision Applications, vol. 13, pp. 398–401 (2009) Google Scholar
  94. 94.
    Lin, H.H., Liu, T.L., Chuang, J.H.: A probabilistic svm approach for background scene initialization. In: Int. Conf. on Image Processing, vol. 3, pp. 893–896 (2002) Google Scholar
  95. 95.
    Lipton, A.J.: Local application of optic flow to analyse rigid versus non-rigid motion. http://www.eecs.lehigh.edu/FRAME/Lipton/ieevframe.html
  96. 96.
    Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proc. of the 4th IEEE Workshop on Applications of Computer Vision, pp. 8–14. IEEE Computer Society, Washington (1998) Google Scholar
  97. 97.
    Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: Sift flow: dense correspondence across different scenes. In: Proc. of the 10th European Conference on Computer Vision: Part III, pp. 28–42. Springer, Berlin, Heidelberg (2008) Google Scholar
  98. 98.
    Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos in the wild. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2009) Google Scholar
  99. 99.
    Lublinerman, R., Ozay, N., Zarpalas, D., Camps, O.: Activity recognition from silhouettes using linear systems and model (in)validation techniques. In: 18th Int. Conf. on Pattern Recognition, vol. 1, pp. 347–350 (2006) Google Scholar
  100. 100.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Int. Joint Conf. on Artificial Intelligence, pp. 674–679. AAAI Press, Menlo Park (1981) Google Scholar
  101. 101.
    Luo, R., Li, L., Gu, I.Y.: Efficient adaptive background subtraction based on multi-resolution background modeling and updating. In: Springer-PCM, pp. 118–127. Springer, Berlin (2007) Google Scholar
  102. 102.
    Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and Viterbi path searching. In: CVPR, Minneapolis, Minnesota, USA, pp. 1–7. IEEE Computer Society, Washington (2007) Google Scholar
  103. 103.
    Ma, X., Grimson, W.E.L.: Edge-based rich representation for vehicle classification. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1185–1192. IEEE Computer Society, Washington (2005) Google Scholar
  104. 104.
    McHugh, J.M., Konrad, J., Saligrama, V., Jodoin, P.M.: Foreground-adaptive background subtraction. IEEE Signal Process. Lett. 16(5), 390–393 (2009) Google Scholar
  105. 105.
    Meyer, F., Bouthemy, P.: Region-based tracking using affine motion models in long image sequences. CVGIP, Image Underst. 60(2), 119–140 (1994) Google Scholar
  106. 106.
    Migdal, J., Grimson, W.E.L.: Background subtraction using Markov thresholds. In: Proc. of the IEEE Workshop on Motion and Video Computing (WACV/MOTION’05), WACV-MOTION ’05, vol. 2, pp. 58–65. IEEE Computer Society, Washington (2005) Google Scholar
  107. 107.
    Minnen, D., Essa, I., Starner, T.: Expectation grammars: leveraging high-level expectations for activity recognition. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 2, pp. 626–632 (2003) Google Scholar
  108. 108.
    Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(03), 231–268 (2001) MATHGoogle Scholar
  109. 109.
    Moeslund, T.B., Hilton, A., kruger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2–3), 90–126 (2006) Google Scholar
  110. 110.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23(4), 349–361 (2001) Google Scholar
  111. 111.
    Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background modeling and subtraction of dynamic scenes. In: Proc. 9th IEEE Int. Conf. on Computer Vision, vol. 2, pp. 1305–1312 (2003) Google Scholar
  112. 112.
    Moore, D., Essa, I.: Recognizing multitasked activities from video using stochastic context-free grammar. In: Proc. AAAI National Conf. on AI, pp. 770–776. AAAI Press, Menlo Park (2002) Google Scholar
  113. 113.
    Moore, D.J., Essa, I.A., Hayes, M.H.: Exploiting human actions and object context for recognition tasks. In: Proc. of 7th IEEE Int. Conf. on Computer Vision, vol. 1, pp. 80–86 (1999) Google Scholar
  114. 114.
    Morris, B.T., Trivedi, M.M.: A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans. Circuits Syst. Video Technol. 18(08), 1114–1127 (2008) Google Scholar
  115. 115.
    Narayana, M., Haverkamp, D.: A Bayesian algorithm for tracking multiple moving objects in outdoor surveillance video. In: CVPR, pp. 1–8. IEEE Press, New York (2007) Google Scholar
  116. 116.
    Natarajan, P., Nevatia, R.: Coupled hidden semi Markov models for activity recognition. In: IEEE Workshop on Motion and Video Computing, pp. 1–8 (2007) Google Scholar
  117. 117.
    Nevatia, R., Hobbs, J., Bolles, B.: An ontology for video event representation. In: IEEE Conf. on Computer Vision and Pattern Recognition Workshop, pp. 119–128 (2004) Google Scholar
  118. 118.
    Nevatia, R., Zhao, T., Hongeng, S.: Hierarchical language-based representation of events in video streams. In: Conf. on Computer Vision and Pattern Recognition Workshop, vol. 4, pp. 39–47 (2003) Google Scholar
  119. 119.
    Nguyen, N.T., Phung, D.Q., Venkatesh, S., Bui, H.: Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 955–960 (2005) Google Scholar
  120. 120.
    Niebles, J.C., Wang, H., Fei-fei, L.: Unsupervised learning of human action categories using spatial-temporal words. In: Proc. British Machine Vision Conference (BMVC) (2006) Google Scholar
  121. 121.
    Niethammer, M., Tannenbaum, A., Angenent, S.: Dynamic active contours for visual tracking. IEEE Trans. Autom. Control 51(4), 562–579 (2006) MathSciNetGoogle Scholar
  122. 122.
    Nowozin, G.S., Bakir, G., Tsuda, K.: Discriminative subsequence mining for action classification. In: ICCV, vol. 11, pp. 1–8. IEEE Press, New York (2007) Google Scholar
  123. 123.
    Ogale, A.S., Karapurkar, A., Aloimonos, Y.: View-invariant modeling and recognition of human actions using grammars. In: 10th Conf. on Category Curve of Long Video, vol. 10, pp. 115–126, Beijing, China. IEEE Press, New York (2005) Google Scholar
  124. 124.
    Oh, S., Hoogs, A., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 3153–3160 (2011) Google Scholar
  125. 125.
    Oikonomopoulos, A., Patras, I., Pantic, M., Paragios, N.: Trajectory-based representation of human actions. In: Artificial Intelligence for Human Computing, vol. 4451, pp. 133–154. Springer, Berlin (2007) Google Scholar
  126. 126.
    Oikonomopoulos, A., Patras, I., Pantici, M.: Spatiotemporal salient points for visual recognition of human actions. IEEE Trans. Syst. Man Cybern. 36(3), 710–719 (2006) Google Scholar
  127. 127.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000) Google Scholar
  128. 128.
    Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Proc. 4th IEEE Int. Conf. on Multimodal Interfaces, pp. 3–8 (2002) Google Scholar
  129. 129.
    Ong, E.J., Gong, S.: The dynamics of linear combinations: tracking 3d skeletons of human subjects. Image Vis. Comput. 20(5/6), 397–414 (2002) Google Scholar
  130. 130.
    Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 266–280 (2000) Google Scholar
  131. 131.
    Paragios, R., Stenger, B., Ramesh, V., Paragios, N., Buhmann, F.C.J.: Topology free hidden Markov models: application to background modeling. In: IEEE Int. Conf. on Computer Vision, pp. 294–301 (2001) Google Scholar
  132. 132.
    Parameswaran, V., Chellappa, R.: View invariance for human action recognition. Int. J. Comput. Vis. 66(1), 83–101 (2006) Google Scholar
  133. 133.
    Parameswaran, V., Singh, M., Ramesh, V.: Illumination compensation based change detection using order consistency. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1982–1989 (2010) Google Scholar
  134. 134.
    Parikh, D., Zitnick, C.L., Chen, T.: Unsupervised learning of hierarchical spatial structures in images. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2009) Google Scholar
  135. 135.
    Park, S., Aggarwal, J.K.: A hierarchical Bayesian network for event recognition of human actions and interactions. Assoc. Comput. Mach. Multimedia Syst. J., 164–179 (2004) Google Scholar
  136. 136.
    Paruchuri, J.K., Sathiyamoorthy, E.P., Ching, S., Cheung, S., Chen, C.H.: Spatially adaptive illumination modeling for background subtraction. In: IEEE Int. Conf. on Computer Vision Workshops (ICCV Workshops), pp. 1745–1752 (2011) Google Scholar
  137. 137.
    Pentland, A.: Smart rooms, smart clothes. In: Proc. Fourteenth Int. Conf. on Pattern Recognition, vol. 2, pp. 949–953 (1998) Google Scholar
  138. 138.
    Peursum, P., West, G., Venkatesh, S.: Combining image regions and human activity for indirect object recognition in indoor wide-angle views. In: 10th IEEE Int. Conf. on Computer Vision, vol. 1, pp. 82–89 (2005) Google Scholar
  139. 139.
    Pilet, J., Strecha, C., Fua, P.: Making background subtraction robust to sudden illumination changes. In: Proc. European Conf. on Computer Vision, pp. 1–14 (2008) Google Scholar
  140. 140.
    Pinhanez, C.S., Bobick, A.F.: Human action detection using pnf propagation of temporal constraints. In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 898–904 (1998) Google Scholar
  141. 141.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Proceedings of Advances in Kernel Methods—Support Vector Learning, pp. 185–208. Microsoft, Redmond (1998) Google Scholar
  142. 142.
    Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28, 976–990 (2010) Google Scholar
  143. 143.
    Porikli, F., Ivanov, Y., Haga, T.: Robust abandoned object detection using dual foregrounds. EURASIP J. Adv. Signal Process. 08, 197875 (2008) Google Scholar
  144. 144.
    Qi, Y., An, G.: Infrared moving targets detection based on optical flow estimation. In: Proc. of IEEE Int. Conf. on Computer Science and Network Technology, pp. 2452–2455 (2011) Google Scholar
  145. 145.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1999) Google Scholar
  146. 146.
    Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: Proc. of the IEEE 11th Int. Conf. on Computer Vision, pp. 1–8 (2007) Google Scholar
  147. 147.
    Rao, C., Shah, M.: View-invariance in action recognition. In: Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 316–322 (2001) Google Scholar
  148. 148.
    Reddy, V., Sanderson, C., Sanin, A., Lovell, B.C.: Adaptive patch-based background modelling for improved foreground object segmentation and tracking. In: 7th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 172–179 (2010) Google Scholar
  149. 149.
    Ren, Y., Chua, C.S.: Bilateral learning for color-based tracking. Image Vis. Comput. 26(11), 1530–1539 (2008) Google Scholar
  150. 150.
    Rodriguez, M.D., Ahmed, J., Shah, M.: Action MACH: a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR. IEEE Press, New York (2008) Google Scholar
  151. 151.
    Rui, Y., Huang, T.S.: Image retrieval: current techniques, promising directions and open issues. J. Vis. Commun. Image Represent. 10, 39–62 (1999) Google Scholar
  152. 152.
    Ryoo, M.S., Aggarwal, J.K.: Recognition of composite human activities through context-free grammar based representation. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 1709–1718 (2006) Google Scholar
  153. 153.
    Ryoo, M.S., Aggarwal, J.K.: Hierarchical recognition of human activities interacting with objects. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007) Google Scholar
  154. 154.
    Ryoo, M.S., Aggarwal, J.K.: Recognition of high-level group activities based on activities of individual members. In: IEEE Workshop on Motion and Video Computing, pp. 1–8 (2008) Google Scholar
  155. 155.
    Ryoo, M.S., Aggarwal, J.K.: Semantic representation and recognition of continued and recursive human activities. Int. J. Comput. Vis. 82, 1–24 (2009) Google Scholar
  156. 156.
    Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: IEEE 12th Int. Conf. on Computer Vision, pp. 1593–1600 (2009) Google Scholar
  157. 157.
    Ryoo, M.S., Aggarwal, J.K.: UT-Interaction Dataset, ICPR contest on Semantic Description of Human Activities (SDHA). http://cvrc.ece.utexas.edu/SDHA2010/Human_Interaction.html (2010)
  158. 158.
    Ryoo, M.S., Chen, C.C., Aggarwal, J.K., Roy-Chowdhury, A.: An overview of contest on semantic description of human activities 2010. In: Proc. Int. Conf. Pattern Recognition Contests, pp. 1–16 (2010) Google Scholar
  159. 159.
    Sakaino, H.: A semitransparency-based optical-flow method with a point trajectory model for particle-like video. IEEE Trans. Image Process. 21(2), 441–450 (2012) MathSciNetGoogle Scholar
  160. 160.
    Salembier, P., Marques, F.: Region-based representations of image and video: segmentation tools for multimedia services. IEEE Trans. Circuits Syst. Video Technol. 9(8), 1147–1169 (1999) Google Scholar
  161. 161.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanoid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005) Google Scholar
  162. 162.
    Schmaltz, C., Rosenhahn, B., Brox, T., Weickert, J.: Localised mixture models in region-based tracking. In: Proc. of the 31st DAGM Symposium on Pattern Recognition, pp. 21–30. Springer, Berlin (2009) Google Scholar
  163. 163.
    Schmaltz, C., Rosenhahn, B., Brox, T., Weickert, J.: Region-based pose tracking with occlusions using 3D models. Mach. Vis. Appl. 23(3), 557–577 (2012) Google Scholar
  164. 164.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proc. IEEE Computer Society Pattern Recognition, vol. 3, pp. 32–36. IEEE Computer Society Press, Los Alamitos (2004) Google Scholar
  165. 165.
    Schunck, B.: The image flow constraint equation. Comput. Vis. Graph. Image Process. 35(1), 20–46 (1986) Google Scholar
  166. 166.
    Schunck, B., Horni, B.: Determining optical flow. In: DARPA81, pp. 144–156 (1981) Google Scholar
  167. 167.
    Sclaroff, S., Isidoro, J.: Active blobs: region-based, deformable appearance models. Comput. Vis. Image Underst. 89(2/3), 197–225 (2003) MATHGoogle Scholar
  168. 168.
    Senst, T., Evangelio, R.H., Sikora, T.: Detecting people carrying objects based on an optical flow motion model. In: IEEE Workshop on Applications of Computer Vision, pp. 301–306 (2011) Google Scholar
  169. 169.
    Shechtman, E., Irani, M.: Space-time behavior based correlation. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 405–412 (2005) Google Scholar
  170. 170.
    Sheikh, Y., Javed, O., Kanade, T.: Background subtraction for freely moving cameras. In: IEEE 12th Int. Conf. on Computer Vision, pp. 1219–1225 (2009) Google Scholar
  171. 171.
    Sheikh, Y., Sheikh, M., Shah, M.: Exploring the space of a human action. In: Tenth IEEE Int. Conf. on Computer Vision, vol. 1, pp. 144–149 (2005) Google Scholar
  172. 172.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600. IEEE Computer Society, Washington (1994) Google Scholar
  173. 173.
    Shi, Y., Huang, Y., Minnen, D., Bobick, A., Essa, I.: Propagation networks for recognition of partially ordered sequential action. In: Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 862–869 (2004) Google Scholar
  174. 174.
    Shibata, M., Yasuda, Y., Ito, M.: Moving object detection for active camera based on optical flow distortion. In: Proc. of the 17th World Congress the International Federation of Automatic Control, Seoul, Korea, pp. 14,720–14,725 (2008) Google Scholar
  175. 175.
    Siskind, J.M.: Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic. J. Artif. Intell. Res. 15, 31–90 (2001) MATHGoogle Scholar
  176. 176.
    Sivic, J., Zisserman, A.: Video data mining using configurations of viewpoint invariant regions. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Washington, DC, pp. 1–8 (2004) Google Scholar
  177. 177.
    Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: Proc. of the 8th ACM Int. Workshop on Multimedia Information Retrieval, Santa Barbara, California, USA, pp. 321–330 (2006) Google Scholar
  178. 178.
    Starner, T., Pentland, A.: Real-time American sign language recognition from video using hidden Markov models. In: Proceedings International Symposium on Computer Vision, pp. 265–270 (1995) Google Scholar
  179. 179.
    Stauffer, C.: Automatic hierarchical classification using time-based co-occurrences. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 333–339 (1999) Google Scholar
  180. 180.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000) Google Scholar
  181. 181.
    Tavakkoli, A., Nicolescu, M., Bebis, G.: A novelty detection approach for foreground region detection in videos with quasi-stationary backgrounds. In: Proc. of the 2nd Int. Symposium on Visual Computing, pp. 40–49. Springer, Berlin, Heidelberg (2006) Google Scholar
  182. 182.
    Techmer, A.: Contour-based motion estimation & object tracking for real-time applications. In: IEEE Image Processing Proceedings, vol. 3, pp. 648–651 (2001) Google Scholar
  183. 183.
    Thi, T.H., Zhang, J., Cheng, L., Wang, L., Satoh, S.: Semi-supervised human action recognition and localization using spatially and temporally integrated local features (2009). http://huetuan.net/semiaction.html
  184. 184.
    Trec Video Retrieval Evaluation Official Website. http://huetuan.net/semiaction.html
  185. 185.
    Tsai, D.M., Lai, S.C.: Independent component analysis-based background subtraction for indoor surveillance. IEEE Trans. Image Process. 18(1), 158–167 (2009) MathSciNetGoogle Scholar
  186. 186.
    Tsuchiya, M., Fujiyoshi, H.: Evaluating feature importance for object classification in visual surveillance. In: Proc. of the 18th Int. Conf. on Pattern Recognition, vol. 2, pp. 978–981. IEEE Computer Society, Washington (2006) Google Scholar
  187. 187.
    Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review. IEE Proc., Vis. Image Signal Process. 152(2), 192–204 (2005) Google Scholar
  188. 188.
    Varcheie, P.D.Z., Sills-Lavoie, M., Bilodeau, G.A.: A multiscale region-based motion detection and background subtraction algorithm. Sensors 10, 1041–1061 (2010) Google Scholar
  189. 189.
    Vaswani, N., Chowdhury, A.R., Chellappa, R.: Activity recognition using the dynamics of the configuration of interacting objects. In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 633–640 (2003) Google Scholar
  190. 190.
    Vaswani, N., Chowdhury, A.R., Chellappa, R.: Shape activity: a continuous state HMM for moving/deforming shapes with application to abnormal activity detection. IEEE Trans. Image Process. 14(10), 1603–1616 (2005) Google Scholar
  191. 191.
    Veeraraghavan, A., Chellappa, R., Roy-Chowdhury, A.K.: The function space of an activity. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 959–968 (2006) Google Scholar
  192. 192.
    Vishwakarma, S., Agrawal, A.: A novel approach for feature quantization using one-dimensional histogram. In: Annual IEEE India Conference (INDICON), pp. 1–4 (2011) Google Scholar
  193. 193.
    Vishwakarma, S., Sapre, A., Agrawal, A.: Action recognition using cuboids of interest points. In: IEEE Int. Conf. on Signal Processing, Communications and Computing (ICSPCC), pp. 1–6 (2011) Google Scholar
  194. 194.
    Vlasic, D., Baran, I., Matusik, W., Popović, J.: Articulated mesh animation from multi-view silhouettes. ACM Trans. Graph. 27(3), 97:1–97:9 (2008) Google Scholar
  195. 195.
    Vogler, C., Metaxas, D.: Parallel hidden Markov models for American sign language recognition. In: IEEE Int. Conf. on Computer Vision, vol. 1, pp. 224–228 (1999) Google Scholar
  196. 196.
    Vosters, L., Shan, C., Gritti, T.: Background subtraction under sudden illumination changes. In: 7th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 384–391 (2010) Google Scholar
  197. 197.
    Vu, V.-T., Bremond, F., Thonnat, M.: Automatic video interpretation: a novel algorithm for temporal scenario recognition. In: Proc. 8th Int. Joint Conf. Artif. Intell, pp. 9–15 (2003) Google Scholar
  198. 198.
    Waltisberg, D., Yao, A., Gall, J., Gool, L.V.: Variations of a hough-voting action recognition system. In: Proc. of Int. Conf. on Pattern Recognition, pp. 1–7 (2010) Google Scholar
  199. 199.
    Wang, J., Bebis, G., Miller, R.: Robust video-based surveillance by integrating target detection with tracking. In: Proc. Conf. on Computer Vision and Pattern Recognition Workshop, CVPRW ’06, pp. 137–144. IEEE Computer Society, Washington (2006) Google Scholar
  200. 200.
    Weber, M.: Unsupervised learning of models for object recognition. Ph.D. thesis, California Institute of Technology, Pasadena, California (2000) Google Scholar
  201. 201.
    Weinland, D., Boyer, E., Ronfard, R.: Action recognition from arbitrary views using 3D exemplars. In: ICCV, Rio de Janeiro, Brazil, vol. 11, pp. 1–7. IEEE Computer Society Press, Los Alamitos (2007) Google Scholar
  202. 202.
    Weinland, D., Ronfard, R., Boyer, E.: Automatic discovery of action taxonomies from multiple views. In: CVPR, vol. 2, pp. 1639–1645. IEEE Computer Society, Washington (2006) Google Scholar
  203. 203.
    Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Comput. Vis. Image Underst. 104(02), 249–257 (2006) Google Scholar
  204. 204.
    Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115, 224–241 (2011) Google Scholar
  205. 205.
    Wen, Z., Cai, Z.: A robust object tracking approach using mean shift. In: 3rd IEEE Int. Conf. on Natural Computation, vol. 2, pp. 170–174 (2007) Google Scholar
  206. 206.
    Wong, S.F., Cipolla, R.: Extracting spatiotemporal interest points using global information. In: ICCV, vol. 11, pp. 1–8. IEEE Press, New York (2007) Google Scholar
  207. 207.
    Wong, S.F., Kim, T.K., Cipolla, R.: Learning motion categories using both semantic and structural information. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–6 (2007) Google Scholar
  208. 208.
    Wunsch, P., Hirzinger, G.: Real-time visual tracking of 3D objects with dynamic handling of occlusion. In: Int. Conf. on Robotics and Automation, 97, Albuquerque, New Mexico, USA, vol. 4, pp. 2868–2879 (1997) Google Scholar
  209. 209.
    Xiang, T.: Video behavior profiling for anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 893–908 (2008) Google Scholar
  210. 210.
    Xiao, J., Cheng, H., Han, F., Sawhney, H.: Geo-spatial aerial video processing for scene understanding and object tracking. In: CVPR, pp. 1–8. IEEE Press, New York (2008) Google Scholar
  211. 211.
    Xu, M., Zuo, L., Iyengar, S., Goldfain, A., DelloStritto, J.: A semi-supervised hidden Markov model-based activity monitoring system. In: 33rd Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, Massachusetts USA, pp. 1794–1797 (2011) Google Scholar
  212. 212.
    Yacoob, Y., Black, M.J.: Parameterized modeling and recognition of activities. In: 6th Int. Conf. on Computer Vision, pp. 120–127 (1998) Google Scholar
  213. 213.
    Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using hidden Markov model. In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 379–385 (1992) Google Scholar
  214. 214.
    Yamazaki, M., Xu, G., Chen, Y.W.: Detection of moving objects by independent component analysis. In: Proc. of the 7th Asian Conf. on Computer Vision, ACCV’06, vol. 2, pp. 467–478. Springer, Berlin, Heidelberg (2006) Google Scholar
  215. 215.
    Yang, F., Li, B.: Unsupervised learning of spatial structures shared among images. Vis. Comput. 28(2), 175–180 (2011) Google Scholar
  216. 216.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 1–45 (2006) Google Scholar
  217. 217.
    Yilmaz, A., Li, X., Shah, M.: Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1531–1536 (2004) Google Scholar
  218. 218.
    Yilmaz, A., Shah, M.: Actions sketch: a novel action representation. In: CVPR, vol. 1, pp. 984–989. IEEE Computer Society, Washington (2005) Google Scholar
  219. 219.
    Yohannes, Y., Hoddinott, J.: Classification and regression trees. Tech. rep., International Food Policy Research Institute, Washington, DC, USA (1999) Google Scholar
  220. 220.
    Yokoyama, M., Poggio, T.: A contour-based moving object detection and tracking. In: 2nd Joint IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 271–276 (2005) Google Scholar
  221. 221.
    Yu, E., Aggarwal, J.K.: Detection of fence climbing from monocular video. In: 18th Int. Conf. on Pattern Recognition, vol. 1, pp. 375–378 (2006) Google Scholar
  222. 222.
    Yu, T.H., Kim, T.K., Cipolla, R.: Real-time action recognition by spatiotemporal semantic and structural forests. In: Proc. of British Machine Vision Conference, pp. 1–7 (2010) Google Scholar
  223. 223.
    Zelnik-Manor, L., Irani, M.: Event-based analysis of video. In: Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 123–130 (2001) Google Scholar
  224. 224.
    Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19(5–6), 345–357 (2008) MATHGoogle Scholar
  225. 225.
    Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Modeling individual and group actions in meetings with layered hmms. IEEE Trans. Multimed. 8(3), 509–520 (2006) Google Scholar
  226. 226.
    Zhang, J., Tian, Y., Yang, Y.: Adaptive dynamic model particle filter for visual object tracking. In: ISECS International Colloquium, vol. 1, pp. 333–336. IEEE Press, New York (2009) Google Scholar
  227. 227.
    Zhang, L., Li, S.Z., Yuan, X., Xiang, S.: Real-time object classification in video surveillance based on appearance learning. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007) Google Scholar
  228. 228.
    Zhao, Y., Gong, H., Lin, L., Jia, Y.: Spatio-temporal patches for night background modeling by subspace learning. In: 19th Int. Conf. on Pattern Recognition, pp. 1–4 (2008) Google Scholar
  229. 229.
    Zhong, H., Shi, J., Visontai, M.: Detecting unusual activity in video. In: Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 819–826 (2004) Google Scholar
  230. 230.
    Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Process. 13(11), 1491–1506 (2004) Google Scholar
  231. 231.
    Zhu, Y., Dariush, B., Fujimura, K.: Kinematic self retargeting: a framework for human pose estimation. Comput. Vis. Image Underst. 114(12), 1362–1375 (2010) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Indian Institute of Information TechnologyAllahabadIndia
  2. 2.BHELHaridwarIndia

Personalised recommendations