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Head-and-Shoulder Detection in Varying Pose

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

Head-and-shoulder detection has been an important research topic in the fields of image processing and computer vision. In this paper, a head-and-shoulder detection algorithm based on wavelet decomposition technique and support vector machine (SVM) is proposed. Wavelet decomposition is used to extract features from real images, and linear SVM and non-linear SVM are trained for detection. Non-head-and-shoulder images can be removed by the linear SVM firstly, and then non-linear SVM detects head-and-shoulder images in detail. Varying head-and-shoulder pose can be detected from frontal and side views, especially from rear view. The experiment results prove that the method proposed is effective and fast to some extent.

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References

  1. Sun, Y., et al.: 2D Recovery of Human Posture. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, pp. 1638–1640 (2002)

    Google Scholar 

  2. Hyeon, D.H., et al.: Human Detection in Images Using Curvature Model. In: International Conference on Circuits/Systems Computers and Communications (ITC-CSCC) (2001)

    Google Scholar 

  3. Broggi, A., et al.: Shape-based Pedestrian Detection. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 215–220 (2000)

    Google Scholar 

  4. Beymer, D., Konolige, K.: Real-time tracking of multiple people using continuous detection. In: Proceedings of IEEE International Conference on Computer Vision (1999)

    Google Scholar 

  5. Govindaraju, V., Srihari, S.N., Sher, D.B.: A computational model for face location. In: Proceedings of the IEEE Third International Conference on Computer Vision, pp. 718–721 (1991)

    Google Scholar 

  6. Govindaraju, V., Sher, D.B., Srihari, R.K.: Locating human faces in newspaper photographs. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 549–554 (1989)

    Google Scholar 

  7. Govindaraju, V., Srihari, S.N., Sher, D.: A computational model for face location based on cognitive principles. In: Proceedings of the American Association for Artificial Intelligence (AAAI), pp. 350–355 (1992)

    Google Scholar 

  8. Govindaraju, V.: Locating human faces in photographs. International Journal of Computer Vision 19, 129–146 (1996)

    Article  Google Scholar 

  9. Papageorgiou, C., Poggio, T.: A Trainable System for Object Detection. International Journal of Computer Vision 38, 15–33 (2000)

    Article  MATH  Google Scholar 

  10. Oren, M., et al.: Pedestrian detection using wavelets templates. In: Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition, pp. 193–199 (1997)

    Google Scholar 

  11. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision, pp. 555–562 (1998)

    Google Scholar 

  12. Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 349–361 (2001)

    Article  Google Scholar 

  13. Fang, J., Qiu, G.: Human face detection using angular radial transform and support vector machines. In: Proceedings of International Conference on Image Processing, vol. 1, pp. 669–672 (2003)

    Google Scholar 

  14. Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  15. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifier. In: Proceedings of 5th ACM Workshop on Computational Learning Theory, pp. 144–152 (1992)

    Google Scholar 

  16. Burges, C.J.C.: Simplified support vector decision rules. In: International Conference on Machine Learning, pp. 71–77 (1996)

    Google Scholar 

  17. Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 1–25 (1995)

    Google Scholar 

  18. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)

    Google Scholar 

  19. Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Proceedings of 10th Conference on Machine Learning (1998)

    Google Scholar 

  20. Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report, Image Speech and Intelligent Systems Research group, University of Southampton (1997)

    Google Scholar 

  21. Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MSR-TR-98-14 (1998)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Sun, Y., Wang, Y., He, Y., Hua, Y. (2005). Head-and-Shoulder Detection in Varying Pose. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_3

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  • DOI: https://doi.org/10.1007/11539117_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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