Abstract
We present an appearance model for establishing correspondence between tracks of people which may be taken at different places, at different times or across different cameras. The appearance model is constructed by kernel density estimation. To incorporate structural information and to achieve invariance to motion and pose, besides color features, an additional feature of path-length is used. To achieve illumination invariance, two types of illumination insensitive color features are discussed: brightness color feature and RGB rank feature. The similarity between a test image and an appearance model is measured by the information gain or Kullback–Leibler distance. To thoroughly represent the information contained in a video sequence with as little data as possible, a key frame selection and matching scheme is proposed. Experimental results demonstrate the important role of the path-length feature in the appearance model and the effectiveness of the proposed appearance model and matching method.
Similar content being viewed by others
References
Comaniciu D., Ramesh V. and Meer P. (2003). Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 564–577
Duda R.O., Stork D.G. and Hart P.E. (2001). Pattern Classification. Wiley, New York
Elgammal, A., , Duraiswami, R., Davis, L.S.: Probabilistic tracking in joint feature-spatial spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2003)
Elgammal A., Duraiswami R., Harwood D. and Davis L.S. (2002). Background and foreground modeling using non-parametric kernel density estimation for visual surveillance. Proc. IEEE 90(7): 1151–1163
Fieguth, Terzopoulos, D.: Color-based tracking of heads and other objects at video frame rates. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (1997)
Garcia J.A., Valdivia J. and Vidal X. (2001). Information theoretic measure for visual target distinctness. IEEE Trans. Pattern Anal. Mach. Intell. 23(4): 362–383
Grossberg M.D. and Nayar S.K. (2003). Determining the camera response from images: what is knowable?. IEEE Trans. Pattern Anal. Mach. Intell. 25(11): 1455–1467
Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2005)
Kang, J., Cohen, I., Medioni, G.: Continuous tracking within and across camera streams. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2003)
Kim K., Chalidabhongse T., Harwood D. and Davis L. (2005). Real-time foreground-background segmentation using codebook model. Real Time Imaging 11(3): 172–185
Kullback S. (1978). Information theory and Statistics. Gloucester Mass, Peter Smith
Li, J., Chellappa, R.: Appearance modeling under geometric context. In: The 10th IEEE International Conference on Computer Vision (2005)
Lin, H., Jacobs, D.: Using the inner-distance for classification of articulated shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2005)
Shan, Y., Sawhney, H., Kumar, R.: Unsupervised learning of discriminative edge measures for vehicle matching between non-overlapping cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2005)
Shan, Y., Sawhney, H., Pope, A.: Measuring the similarity of two image sequences. In: Asia Conference on Computer Vision (2004)
Yamagunchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: International Conference on Automatic Face and Gesture Recognition (1998)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yu, Y., Harwood, D., Yoon, K. et al. Human appearance modeling for matching across video sequences. Machine Vision and Applications 18, 139–149 (2007). https://doi.org/10.1007/s00138-006-0061-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-006-0061-z