Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection

  • Tomoki Watanabe
  • Satoshi Ito
  • Kentaro Yokoi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

The purpose of this paper is to detect pedestrians from images. This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG). Including co-occurrence with various positional offsets, the feature descriptors can express complex shapes of objects with local and global distributions of gradient orientations. Our method is evaluated with a simple linear classifier on two famous pedestrian detection benchmark datasets: “DaimlerChrysler pedestrian classification benchmark dataset” and “INRIA person data set”. The results show that proposed method reduces miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets.

Keywords

Pedestrian detection CoHOG co-occurrence histograms of oriented gradients co-occurrence matrix 

References

  1. 1.
    Gavrila, D., Philomin, V.: Real-time object detection for “smart” vehicles. In: The Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 87–93. IEEE Computer Society Press, Los Alamitos (1999)CrossRefGoogle Scholar
  2. 2.
    Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1863–1868 (2006)CrossRefGoogle Scholar
  3. 3.
    Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comput. Vision 73(1), 41–59 (2007)CrossRefGoogle Scholar
  4. 4.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: The Ninth IEEE International Conference on Computer Vision, Washington, DC, USA, pp. 734–741. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar
  5. 5.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23(4), 349–361 (2001)CrossRefGoogle Scholar
  6. 6.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vision 38(1), 15–33 (2000)CrossRefMATHGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 257–263 (2003)Google Scholar
  10. 10.
    Winder, S.A.J., Brown, M.: Learning local image descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  11. 11.
    Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: single-frame classification and system level performance. In: IEEE Intelligent Vehicles Symposium, pp. 1–6 (2004)Google Scholar
  12. 12.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: The Tenth IEEE International Conference on Computer Vision, Washington, DC, USA, vol. 1, pp. 90–97. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  15. 15.
    Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  16. 16.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  17. 17.
    Hsieh, C., Chang, K., Lin, C., Keerthi, S., Sundararajan, S.: A dual coordinate descent method for large-scale linear svm. In: McCallum, A., Roweis, S. (eds.) The 25th Annual International Conference on Machine Learning, pp. 408–415. Omnipress (2008)Google Scholar
  18. 18.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Taipei (2003)Google Scholar
  19. 19.
    Joachims, T.: Training linear svms in linear time. In: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226 (2006)Google Scholar
  20. 20.
    Dollar, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  21. 21.
    Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tomoki Watanabe
    • 1
  • Satoshi Ito
    • 1
  • Kentaro Yokoi
    • 1
  1. 1.Corporate Research and Development CenterTOSHIBA CorporationKawasakiJapan

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