SVM-BASED HUMAN DETECTION COMBINING SELF-QUOTIENT ε-FILTER AND HISTOGRAMS OF ORIENTED GRADIENTS

Mitsuharu Matsumoto

2010

Abstract

This paper describes a noise robust SVM-based human detection combining self-quotient ε-filter (SQEF) and histograms of oriented gradients (HOG). Although human detection combining HOG and SVM is a powerful approach, as it uses local intensity gradients, it is difficult to handle noise corrupted images. To handle noise corrupted images, we introduce self-quotient ε-filter (SQEF), and implement it in human detection combining HOG and SVM. SQEF is an advanced self-quotient filter (SQF), and can clearly extract features from the images not only when they have illumination variations but also when they are corrupted with noise. The new approach gives a robust human detection from noise corrupted images using the data trained by intact images without noise.

References

  1. Arakawa, K. and Okada, T. (2005). e -separating nonlinear filter bank and its application to face image beautification. In IEICE Transactions on Fundamentals, pages 1216-1225. IEICE.
  2. Belongie, S., Malik, J., and Puzicha, J. (2001). Matching shapes. In Proc. of Int'l Conf. on Computer Vision, pages 454-461. IEEE.
  3. Chang, C.-C. and Lin, C.-J. (2001). LIBSVM: a library for support vector machines. In http://www.csie.ntu.edu.tw/ cjlin/libsvm.
  4. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proc. of Int'l Conf. on Computer Vision and Pattern Recognition., pages 886-893. IEEE.
  5. Freeman, W. T. and Roth, M. (1995). Orientation histograms for hand gesture recognition. In Proc. of Int'l Workshop on Automatic Face and Gesture Recognition, pages 296-301. IEEE.
  6. Freeman, W. T., Tanaka, K., Ohta, J., and Kyuma, K. (1996). Computer vision for computer games. In Proc. of Int'l Conf. on Automatic Face and Gesture Recognition, pages 100-105. IEEE.
  7. Gooch, B., Reinhard, E., and Gooch, A. (2004). Human facial illustrations: Creations and psychological evaluation. In ACM transactions on Graphics, pages 27-44. ACM.
  8. Himayat, N. and Kassam, S. (1993). Approximate performance analysis of edge preserving filters. In IEEE Trans. on Signal Processing., pages 2764-2777. IEEE.
  9. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. In Int'l Journal of Computer Vision, volume 60, pages 91-110. Springer.
  10. Matsumoto, M. (2010). Feature extraction from noisy face image using self-quotient e -filter. Proc. of Int'l Conf. on Computer Engineering and Technology, pages 395-399.
  11. Mohan, A., Papageorgiou, C., and Poggio, T. (2001). Example-based object detection in images by components. In IEEE Trans. on PAMI., volume 23, pages 349-361. IEEE.
  12. Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio, T. (1997). Pedestrian detection using wavelet templates. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pages 193-199. IEEE.
  13. Papageorgiou, C., Oren, M., and Poggio, T. (1998). A general framework for object detection. In Proc. of Int'l Conf. on Computer Vision, pages 555-562. IEEE.
  14. Papageorgiou, C. and Poggio, T. (2000). A trainable system for object detection. In Int'l Journal of Computer Vision, volume 38, pages 15-33. Springer.
  15. Tomasi, C. and Manduchi, R. (1998). Bilateral filtering for gray and color images. In Proc. of Int'l Conf. on Computer Vision. IEEE.
  16. Viola, P., Jones, M. J., and Snow, D. (2003). Detecting pedestrians using patterns of motion and appearance. In Proc. of Int'l Conf. on Computer Vision, volume 1, pages 734-741. IEEE.
  17. Wang, H., Li., S. Z., and Wang, Y. (2004a). Face recognition under varying lighting conditions using self quotient image. In Proc. of Int'l Conf. on Automation Face and Gesture Recognition. IEEE.
  18. Wang, H., Zhang, J. J., Li., S. Z., and Wang, Y. (2004b). Shape and texture preserved non-photorealistic rendering. In Computer animation and virtual worlds, pages 453-461. John Wiley and Sons, Ltd.
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Paper Citation


in Harvard Style

Matsumoto M. (2010). SVM-BASED HUMAN DETECTION COMBINING SELF-QUOTIENT ε-FILTER AND HISTOGRAMS OF ORIENTED GRADIENTS . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 241-245. DOI: 10.5220/0003055002410245


in Bibtex Style

@conference{icnc10,
author={Mitsuharu Matsumoto},
title={SVM-BASED HUMAN DETECTION COMBINING SELF-QUOTIENT ε-FILTER AND HISTOGRAMS OF ORIENTED GRADIENTS},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={241-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003055002410245},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - SVM-BASED HUMAN DETECTION COMBINING SELF-QUOTIENT ε-FILTER AND HISTOGRAMS OF ORIENTED GRADIENTS
SN - 978-989-8425-32-4
AU - Matsumoto M.
PY - 2010
SP - 241
EP - 245
DO - 10.5220/0003055002410245