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Robust Human Detection Using Multiple Scale of Cell Based Histogram of Oriented Gradients and AdaBoost Learning

  • Van-Dung Hoang
  • My-Ha Le
  • Kang-Hyun Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7653)

Abstract

Human detection is an important task in many applications such as intelligent transport systems, surveillance systems, automatic human assistance systems, image retrieval, and so on. This paper proposes a multiple scale of cell based Histogram of Oriented Gradients (HOG) features description for human detection system. Using these proposed feature descriptors, a robust system is developed according to decision tree structure of boosting algorithm. In this system, the integral image based method is utilized to compute feature descriptors rapidly, and then cascade classifiers are taken into account to reduce computational cost. The experiments were performed on INRIA’s database and our own database, which includes samples in several different sizes. The experiment results showed that our proposed method produce high performance with lower false positive and higher recall rate than the standard HOG features description. This method is also efficient with different resolution and gesture poses under a variety of backgrounds, lighting, as well as individual human in crowds, and partial occlusions.

Keywords

Cascade boosting multiple cell scale based HOG human detection 

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References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition, vol. 881(1), pp. 886–893 (2005)Google Scholar
  2. 2.
    Wang, C.-C.R., Lien, J.-J.J.: AdaBoost learning for human detection based on histograms of oriented gradients. In: Asian Conference on Computer Vision, pp. 885–895 (2007)Google Scholar
  3. 3.
    Qiang, Z., Mei-Chen, Y., Kwang-Ting, C., Avidan, S.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491–1498 (2006)Google Scholar
  4. 4.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian Detection: An Evaluation of the State of the Art. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 743–761 (2012)CrossRefGoogle Scholar
  5. 5.
    Enzweiler, M., Gavrila, D.M.: Monocular Pedestrian Detection: Survey and Experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2179–2195 (2009)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: International Conference on Computer Vision, vol. 2, pp. 734–741 (2003)Google Scholar
  7. 7.
    Munder, S., Gavrila, D.M.: An Experimental Study on Pedestrian Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1863–1868 (2006)CrossRefGoogle Scholar
  8. 8.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: International Conference on Image Processing, vol. 1, pp. 900–903 (2002)Google Scholar
  9. 9.
    Viola, P., Jones, M.J.: Robust Real-Time Face Detection. Intenational Journal of Compute Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  10. 10.
    Papageorgiou, C., Poggio, T.: A Trainable System for Object Detection. Intenational Journal Compute Vision 38, 15–33 (2000)zbMATHCrossRefGoogle Scholar
  11. 11.
    Gavrila, D.M., Giebel, J., Munder, S.: Vision-based pedestrian detection: The protector system. In: IEEE Intelligent Vehicles Symposium, pp. 13–18 (2004)Google Scholar
  12. 12.
    Gerónimo, D., Sappa, A.D., López, A., Ponsa, D.: Pedestrian Detection using Adaboost Learning of Features and Vehicle Pitch Estimation. In: International Conference on Visualization, Imaging, and Image Processing, pp. 40–405 (2006)Google Scholar
  13. 13.
    Gerónimo, D., López, A., Ponsa, D., Sappa, A.D.: Haar Wavelets and Edge Orientation Histograms for On–Board Pedestrian Detection. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007, Part I. LNCS, vol. 4477, pp. 418–425. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: International Conference on Computer Vision, pp. 32–39 (2009)Google Scholar
  15. 15.
    Satpathy, A., Xudong, J., How-Lung, E.: Extended Histogram of Gradients feature for human detection. In: IEEE International Conference on Image Processing, pp. 3473–3476 (2010)Google Scholar
  16. 16.
    Chih-Chung, C., Chih-Jen, L.: LIBSVM: a Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Van-Dung Hoang
    • 1
  • My-Ha Le
    • 1
  • Kang-Hyun Jo
    • 1
  1. 1.School of Electrical EngineeringUniversity of UlsanKorea

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