Advertisement

Speeding Up HOG and LBP Features for Pedestrian Detection by Multiresolution Techniques

  • Philip Geismann
  • Alois Knoll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)

Abstract

In this article, we present a fast pedestrian detection system for driving assistance. We use current state-of-the-art HOG and LBP features and combine them into a set of powerful classifiers. We propose an encoding scheme that enables LBP to be used efficiently with the integral image approach. This way, HOG and LBP block features can be computed in constant time, regardless of block position or scale. To further speed up the detection process, a coarse-to-fine scanning strategy based on input resolution is employed. The original camera resolution is consecutively downsampled and fed to different stage classifiers. Early stages in low resolutions reject most of the negative candidate regions, while few samples are passed through all stages and are evaluated by more complex features. Results presented on the INRIA set show competetive accuracy performance, while both processing and training time of our system outperforms current state-of-the-art work.

Keywords

Local Binary Pattern Integral Image Pedestrian Detection Local Binary Pattern Feature Integral Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 893–886 (2005)Google Scholar
  2. 2.
    Geronimo, D., Lopez, A., Ponsa, D., Sappa, A.: Haar wavelets and edge orientation histograms for On Board pedestrian detection. In: Pattern Recognition and Image Analysis, pp. 418–425 (2007)Google Scholar
  3. 3.
    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
  4. 4.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 304–311 (2009)Google Scholar
  5. 5.
    Mu, Y., Yan, S., Liu, Y., Huang, T., Zhou, B.: Discriminative local binary patterns for human detection in personal album. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)Google Scholar
  6. 6.
    Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: IEEE International Conference on Computer Vision, ICCV 2009 (2009)Google Scholar
  7. 7.
    Agarwal, A., Triggs, W.: Hyperfeatures - multilevel local coding for visual recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 30–43. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1713–1727 (2008)CrossRefGoogle Scholar
  9. 9.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Ninth IEEE International Conference on Computer Vision, Proceedings, vol. 2, pp. 734–741 (2003)Google Scholar
  10. 10.
    Zhu, Q., Avidan, S., chen Yeh, M., ting Cheng, K.: Fast human detection using a cascade of histograms of oriented gradients. In: CVPR 2006, pp. 1491–1498 (2006)Google Scholar
  11. 11.
    Chen, Y., Chen, C.: Fast human detection using a novel boosted cascading structure with meta stages. IEEE Transactions on Image Processing 17, 1452–1464 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Geismann, P., Schneider, G.: A two-staged approach to vision-based pedestrian recognition using haar and HOG features. In: 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, Netherlands, pp. 554–559 (2008)Google Scholar
  13. 13.
    Zhang, W., Zelinsky, G., Samaras, D.: Real-time accurate object detection using multiple resolutions. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8 (2007)Google Scholar
  14. 14.
    Porikli, F.: Integral histogram: A fast way to extract histograms in cartesian spaces. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 829–836 (2005)Google Scholar
  15. 15.
    Lowe, D.G.: Distinctive image features from Scale-Invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Philip Geismann
    • 1
  • Alois Knoll
    • 1
  1. 1.Robotics and Embedded SystemsTechnische Universität MünchenGarchingGermany

Personalised recommendations