Skip to main content
SpringerLink
Log in

Estimation of Pedestrian Pose and Orientation Using on-Board Camera with Histograms of Oriented Gradients Features

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Understanding pedestrian behavior, including head and body orientation, is important for a pedestrian safety system. In this paper, we propose an approach that estimates head pose and body orientation by considering two constraints, the pedestrian model constraint between head and body directions and the temporal constraint. In our approach, given an image of pedestrian, image features are extracted and estimates are made of the probabilities of the head position, size and orientation, and the body orientation; these are obtained using a multi-class classifier and tracked by particle filter. We applied two constraints to the particle filter to achieve more accurate estimate. Experiments using real videos from an on-board monocular camera show the effectiveness of our approach.

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. Oliveira, L., Nunes, U.:“Context-aware Pedestrian Detection Using LIDAR”, IEEE Intelligent Vehicles Symposium (IV), pp.773–778 (2010)

  2. Nedevschi, S., Bota, S., Tomiuc, C.: Stereo-based pedestrian detection for collision-avoidance applications”. IEEE Trans Intell Transp Syst 10(3), 380–391 (2009)

    Article  Google Scholar 

  3. Kidono, K., Naito, T., Miura, J.:“Reliable pedestrian recognition combining high-definition LIDAR and vision data”, IEEE Conference on Intelligent Transportation Systems, pp.1783–1788 (2012)

  4. Andriluka, M., Roth, S., Schiele, B.:“People-tracking-by-detection and people-detection-by-tracking”, IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8 (2008)

  5. Wojek, C., Walk, W., Schiele, B.:“Multi-cue onboard pedestrian detection”, IEEE Conference on Computer Vision and Pattern Recognition, pp.794–801 (2009)

  6. Garvila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle”. Int. J. Comput. Vis. 73(1), 41–59 (2007)

    Article  Google Scholar 

  7. Dalal, N., Triggs, B.: “Histograms of oriented gradients for human detection”. IEEE Conf Comput Vision Pattern Recog 1, 886–893 (2005)

    Google Scholar 

  8. Enzweiler, M., Garvrila, D.M.: Monocular pedestrian detection: survey and experiments”. IEEE Trans Pattern Analysis Mach Intell 31(12), 2179–2195 (2009)

    Article  Google Scholar 

  9. HyungKwan, K., Yuuki, S., Shunsuke, K.: “Precise segmantaion and estimation of pedestrian trajectory using on-board monocular camaera”, IEICE ITS special issue, IEICE TRANSACTIONS on fundamentals of electronics. Commun Comput Sci E95-A(1), 296–304 (2012)

    Google Scholar 

  10. HyungKwan Kim, Yuki Shibayama and Shunsuke Kamijo, “Precise Segmentation and Position Estimation of Pedestrians by the combination of the HOG Classifier and S-T MRF Model”, The Transportation Research Board (TRB) 91st Annual Meeting, Paper#12–4526 (2012)

  11. Andriluka, M., Roth, S., Schiele, B.:“Monocular 3d pose estimation and tracking by detection”, IEEE Conference on Computer Vision and Pattern Recognition, pp.623–630 (2010)

  12. Tosato, D., Farenzena, M., Spera, M., Murino, V., Cristani, M.:“Multi-class classification on Riemannian Manifolds for Video Surveillance”, European Conference on Computer Vision, pp.378–391 (2010)

  13. Schulz, A., Stiefelhagen, R. “Video-based pedestrian head pose estimation for risk assessment”, International IEEE Conference on Intelligent Transportation Systems, pp.1771–1776 (2012)

  14. Viola, P., Jones, M.: “Rapid object detection using a boosted cascade of simple features”. Proc 2001 I.E. Comput Soc Conf Comput Vision Pattern Recog 1, 511–518 (2001)

    Google Scholar 

  15. Viola, P., Jones, M.: Robust real-time face detection”. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  16. Ojala, T., Pietikainen, M., Maenpaa T.:“Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Transaction on Pattern Analysis and Machine Intelligence, pp.971–987 (2002)

  17. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition”. IEEE Trans Pattern Analysys Mach Intell 28(12), 2037–2041 (2006)

    Article  MATH  Google Scholar 

  18. Shan, C., Gong, S., Peter, W.:“Robust facial expression recognition using local binary patterns”, IEEE International Conference on Image Processing, pp370–373 (2005)

  19. Maturana, D., Mery, D., Soto A.:“Face Recognition with Decision Tree-based Local Binary Patterns”, ComputerVision – ACCV 2010, pp.618–629 (2011)

  20. Riccia, E., Odobez, J.:“Learning large margin likelihoods for realtime head pose tracking”, IEEE International Conference on Image Processing, pp.2593–2596 (2009)

  21. Chang, C.-C., Lin, C.-J.: ACM transactions on intelligent systems and technology. ACM Trans Intell Syst Technol 2(3), 27:1–27:27 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan

    Shinya Yano, Yanlei Gu & Shunsuke Kamijo

Authors
  1. Shinya Yano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanlei Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shunsuke Kamijo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shinya Yano.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yano, S., Gu, Y. & Kamijo, S. Estimation of Pedestrian Pose and Orientation Using on-Board Camera with Histograms of Oriented Gradients Features. Int. J. ITS Res. 14, 75–84 (2016). https://doi.org/10.1007/s13177-014-0103-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-014-0103-2

Keywords

Search

Navigation