Skip to main content
SpringerLink
Log in

Categorization-based two-stage pedestrian detection system for naturalistic driving data

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Understanding pedestrian behavior is a key aspect in the design and testing of a pedestrian pre-collision system. Large-scale naturalistic driving data analysis can provide valuable and objective information on how pedestrians behave in real life. Analyzing pedestrian behavior within large-scale naturalistic driving data requires an efficient pedestrian detection method. However, detecting pedestrians from large-scale naturalistic driving data is challenging due to the high pedestrian appearance variance and fast changing complicated background. In this paper, a categorization-based two-stage pedestrian detection system is proposed to efficiently locate pedestrians within our collected TASI 110-car naturalistic driving dataset. Category information including vehicle status, location, and time is automatically extracted, and efficient category-specific detection algorithms are designed for different scenario categories. The experimental results on the test set show the effectiveness of the proposed approach compared to traditional pedestrian detection methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. National Highway Traffic Safety Administration, USDOT: TRAFFIC SAFETY FACTS 2010 A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System. DOT HS 811 659 (2010)

  2. Gandhi, T., Trivedi, M.M.: Pedestrian protection systems: issues, survey, and challenges. IEEE Trans. Intell. Transp. Syst. 8, 413–430 (2007)

    Article  Google Scholar 

  3. Geronimo, D., Lopez, A.M., Sappa, A.D., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1239–1258 (2010)

    Article  Google Scholar 

  4. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2179–2195 (2009)

    Article  Google Scholar 

  5. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743–761 (2012)

    Article  Google Scholar 

  6. Huang, G.-B., Chen, L., Siew, C.-K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17, 879–892 (2006)

    Article  Google Scholar 

  7. Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)

    Article  MathSciNet  Google Scholar 

  8. Sanger, T.D.: Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Netw. 2, 459–473 (1989)

    Article  Google Scholar 

  9. Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comput. Vis. 73, 41–59 (2007)

    Article  Google Scholar 

  10. Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE Trans. Pattern Anal. Mach. Intell. 32, 604–618 (2010)

    Article  Google Scholar 

  11. Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vis. 38, 15–33 (2000)

    Article  MATH  Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, pp. 886–893 (2005)

  13. Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1–8 (2007)

  14. Wu, B., Nevatia, R.: Simultaneous object detection and segmentation by boosting local shape feature based classifier. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1–8 (2007)

  15. Gerónimo, D., Sappa, A., López, A., Ponsa, D.: Adaptive image sampling and windows classification for on-board pedestrian detection. In: Proceedings of the International Conference on Computer Vision Systems, Bielefeld, Germany (2007)

  16. Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. Pattern Recognit. 82–91 (2008)

  17. Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: British Machine Vision Conference, 2009, pp. 1–11 (2009)

  18. Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west, BMVC 2010. Aberystwyth, UK (2010)

  19. Dollár, P., Appel, R., Kienzle, W.: Crosstalk cascades for frame-rate pedestrian detection. In: ECCV Florence, Italy (2012)

  20. Xu, Y., Xu, D., Lin, S., Han, T.X., Cao, X., Li, X.: Detection of sudden pedestrian crossings for driving assistance systems. IEEE Trans. Syst. Man Cybern. B Cybern. 42, 729–739 (2012)

    Article  Google Scholar 

  21. Enzweiler, M., Gavrila, D.M.: A multilevel mixture-of-experts framework for pedestrian classification. IEEE Trans. Image Process. 20, 2967–2979 (2011)

    Article  MathSciNet  Google Scholar 

  22. Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23, 349–361 (2001)

    Article  Google Scholar 

  23. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)

    Article  Google Scholar 

  24. Lin, Z., Davis, L.: A pose-invariant descriptor for human detection and segmentation. Computer Vision–ECCV 2008, pp. 423–436 (2008)

  25. Maji, S., Berg, A.C., Malik, J., Classification using intersection kernel support vector machines is efficient. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8 (2008)

  26. Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63, 153–161 (2005)

    Article  Google Scholar 

  27. Freund, Y., Schapire, R.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory, 1995, pp. 23–37 (1995)

  28. Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1713–1727 (2008)

    Article  Google Scholar 

  29. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28, 337–407 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  30. Overett, G., Petersson, L., Brewer, N., Andersson, L., Pettersson, N.: A new pedestrian dataset for supervised learning. In: IEEE Intelligent Vehicles Symposium, 2008, pp. 373–378 (2008)

  31. Gavrila, D., Giebel, J., Munder, S.: Vision-based pedestrian detection: the protector system. In: IEEE Intelligent Vehicles Symposium, 2004, pp. 13–18 (2004)

  32. Zhao, L., Thorpe, C.E.: Stereo-and neural network-based pedestrian detection. IEEE Trans. Intell. Transp. Syst. 1, 148–154 (2000)

    Article  Google Scholar 

  33. Gavrila, D.M., Giebel, J.: Shape-based pedestrian detection and tracking. In: IEEE Intelligent Vehicle Symposium, 2002, pp. 8–14 (2002)

  34. Geismann, P., Schneider, G.: A two-staged approach to vision-based pedestrian recognition using Haar and HOG features. In: IEEE Intelligent Vehicles Symposium, 2008, pp. 554–559 (2008)

  35. Oliveira, L., Nunes, U., Peixoto, P.: On exploration of classifier ensemble synergism in pedestrian detection. IEEE Trans. Intell. Transp. Syst. 11, 16–27 (2010)

    Article  Google Scholar 

  36. Yang, K., Du, E.Y., Pingge, J., Yaobin, C., Sherony, R., Takahashi, H.: Automatic categorization-based multi-stage pedestrian detection. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 451–456 (2012)

  37. Kai, Y., Du, E.Y., Delp, E.J., Pingge, J., Feng, J., Yaobin, C., Sherony, R., Takahashi, H.: An extreme learning machine-based pedestrian detection method. In: IEEE Intelligent Vehicles Symposium (IV) 2013, pp. 1404–1409 (2013)

  38. Shafer, G.: A Mathematical Theory of Evidence, vol. 76. Princeton university press, Princeton (1976)

    MATH  Google Scholar 

  39. Crow, F.C.: Summed-area tables for texture mapping. Comput. Graph. 18, 207–212 (1984)

    Article  Google Scholar 

  40. Wang, X., Han, T. X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, 2009, pp. 32–39 (2009)

  41. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88, 303–338 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Kai Yang & Eliza Du

  2. Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    E. J. Delp

  3. Qualcomm Inc., Santa Clara, CA, 95014, USA

    Eliza Du

Authors
  1. Kai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. J. Delp
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eliza Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, K., Delp, E.J. & Du, E. Categorization-based two-stage pedestrian detection system for naturalistic driving data. SIViP 8 (Suppl 1), 135–144 (2014). https://doi.org/10.1007/s11760-014-0699-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0699-3

Keywords

Search

Navigation