Abstract
In intelligent surveillance systems, recognition of humans and their activities is generally the most important task. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. For individual recognition, this report considers two different categories. First, individual recognition using “style of walk” i.e. gait and second “style of doing similar actions” in video sequences. The “style of walk” and “style of actions” are proposed as a cue to discriminate between two individuals. The “style of walk” and “style of actions” for each individual is called their “body language” information.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Johansson, G.: Visual motion perception. Science American 232(6), 76–88 (1975)
Cutting, J., Prott, D., Kozlowski, L.: A biomechanical invariant for gait perception. Journal of Experimental Psychology: Human Perception and Performance 4(3), 357–372 (1978)
Little, J.J., Boyd, J.E.: Recognizing people by their gait: The shape of motion. Computer Vision Research 1(2), 1–32 (1998)
Nixon, M., Carter, J., Nash, J., Huang, P., Cunado, D., Stevenage, S.: Automatic gait recognition. IEE Colloquium Motion Analysis and Tracking, 31-36 (1999)
Johnson, A., Bobick, A.: Gait recognition using static, activity specific parameters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 423–430 (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson Education, London
Sirovich,, Kirby, M.: Low dimensional procedure for the charecterization of human faces. Journal of Optical Society of America 4(3), 519–524 (1987)
Nayar, S.K., Murase, H., Nene, S.A.: Parametric appearance representation in early visual learning, ch. 6. Oxford University Press, Oxford (1996)
Murase, H., Sakai, R.: Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters 17(2), 155–162 (1996)
He, Q., Debrunner, C.H.: Individual recognition from periodic activity using hidden markov models. In: Proceedings IEEE Workshop on Human Motion, pp. 47–52 (2000)
Guo, A., Siegelmann, H.: Time-warped longest common subsequence algorithm for music retrieval. In: Proc. Fifth International Conference on Music Information Retrieval, pp. 10–14 (2004)
Condell, J.V., Scotney, B.W., Morrow, P.J.: Adaptive Grid Refinement Procedures for Efficient Optical Flow Computation. International Journal of Computer Vision (1), 31–54 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pratheepan, Y., Torr, P.H.S., Condell, J.V., Prasad, G. (2008). Body Language Based Individual Identification in Video Using Gait and Actions. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds) Image and Signal Processing. ICISP 2008. Lecture Notes in Computer Science, vol 5099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69905-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-69905-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69904-0
Online ISBN: 978-3-540-69905-7
eBook Packages: Computer ScienceComputer Science (R0)
