Skip to main content
Log in

Human appearance modeling for matching across video sequences

Machine Vision and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Comaniciu D., Ramesh V. and Meer P. (2003). Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 564–577

    Article  Google Scholar 

  2. Duda R.O., Stork D.G. and Hart P.E. (2001). Pattern Classification. Wiley, New York

    MATH  Google Scholar 

  3. 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)

  4. 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

    Article  Google Scholar 

  5. 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)

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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)

  9. 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)

  10. 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

    Article  Google Scholar 

  11. Kullback S. (1978). Information theory and Statistics. Gloucester Mass, Peter Smith

    Google Scholar 

  12. Li, J., Chellappa, R.: Appearance modeling under geometric context. In: The 10th IEEE International Conference on Computer Vision (2005)

  13. 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)

  14. 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)

  15. Shan, Y., Sawhney, H., Pope, A.: Measuring the similarity of two image sequences. In: Asia Conference on Computer Vision (2004)

  16. Yamagunchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: International Conference on Automatic Face and Gesture Recognition (1998)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Larry S. Davis.

Rights and permissions

Reprints 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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-006-0061-z

Keywords

Navigation