Abstract
We propose a method of gait identification based on multi-view gait images using an omnidirectional camera. We first transform omnidirectional silhouette images into panoramic ones and obtain a spatio-temporal Gait Silhouette Volume (GSV). Next, we extract frequency- domain features by Fourier analysis based on gait periods estimated by autocorrelation of the GSVs. Because the omnidirectional camera makes it possible to observe a straight-walking person from various views, multi-view features can be extracted from the GSVs composed of multi-view images. In an identification phase, distance between a probe and a gallery feature of the same view is calculated, and then these for all views are integrated for matching. Experiments of gait identification including 15 subjects from 5 views demonstrate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Urtasun, R., Fua, P.: 3d tracking for gait characterization and recognition. In: Proc. of the 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 17–22. IEEE Computer Society Press, Los Alamitos (2004)
Yam, C., Nixon, M., Carter, J.: Automated person recognition by walking and running via model-based approaches. Pattern Recognition 37(5), 1057–1072 (2004)
Sarkar, S., Phillips, J., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanid gait challenge problem: Data sets, performance, and analysis. Trans. of Pattern Analysis and Machine Intelligence 27(2), 162–177 (2005)
Han, J., Bhanu, B.: Individual recognition using gait energy image. Trans. on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)
Yu, S., Tan, D., Tan, T.: Modelling the effect of view angle variation on appearance-based gait recognition. In: Proc. of 7th Asian Conf. on Computer Vision, vol. 1, pp. 807–816 (2006)
Kale, A., Roy-Chowdhury, A., Chellappa, R.: Towards a view invariant gait recognition algorithm. In: Proc. of IEEE Conf. on Advanced Video and Signal Based Surveillance, pp. 143–150. IEEE Computer Society Press, Los Alamitos (2003)
Shakhnarovich, G., Lee, L., Darrell, T.: Integrated face and gait recognition from multiple views. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 439–446 (2001)
Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Proc. of the 9th European Conf. on Computer Vision, Graz, Austria, vol. 3, pp. 151–163 (2006)
Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Which reference view is effective for gait identification using a view transformation model? In: Proc. of the IEEE Computer Society Workshop on Biometrics 2006, New York, USA (2006)
Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Adaptation to walking direction changes for gait identification. In: Proc. of the 18th Int. Conf. on Pattern Recognition, Hong Kong, China, vol. 2, pp. 96–99 (2006)
Yamazawa, K., Yagi, Y., Yachida, M.: Hyperomni vision: Visual navigation with an omnidirectional image sensor. Systems and Computers in Japan 28(4), 36–47 (1997)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face-recognition algorithms. Trans. of Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sugiura, K., Makihara, Y., Yagi, Y. (2007). Gait Identification Based on Multi-view Observations Using Omnidirectional Camera. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-76386-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
Online ISBN: 978-3-540-76386-4
eBook Packages: Computer ScienceComputer Science (R0)