Wheelchair-user Detection Combined with Parts-based Tracking

Ukyo Tanikawa, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Ryo Kawai

Abstract

In recent years, there has been an increasing demand for automatic wheelchair-user detection from a surveillance video to support wheelchair users. However, it is difficult to detect them due to occlusions by surrounding pedestrians in a crowded scene. In this paper, we propose a detection method of wheelchair users robust to such occlusions. Concretely, in case the detector cannot a detect wheelchair user, the proposed method estimates his/her location by parts-based tracking based on parts relationship through time. This makes it possible to detect occluded wheelchair users even though he/she is heavily occluded. As a result of an experiment, the detection of wheelchair users with the proposed method achieved the highest accuracy in crowded scenes, compared with comparative methods.

References

  1. Bolme, D. S., Beveridge, J. R., Draper, B., and Lui, Y. M. (2010). Visual object tracking using adaptive correlation filters. In Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2544-2550.
  2. Bolme, D. S., Draper, B. A., and Beveridge, J. R. (2009). Average of synthetic exact filters. In Proceedings of the 22nd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2105- 2112.
  3. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of the 18th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 886- 893.
  4. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1627-1645.
  5. Girshick, R. B., Felzenszwalb, P. F., and McAllester, D. (2012). Discriminatively trained deformable part models, release 5. Available at: http://people.cs.uchicago.edu/~rbg/latent-release5/ [Accessed 14 Sept. 2016].
  6. Henriques, J. F., Caseiro, R., Martins, P., and Batista, J. (2015). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3):583-596.
  7. Huang, C.-R., Chen, C.-S., and Chung, P.-C. (2006). Contrast context histogram - A discriminating local descriptor for image matching. In Proceedings of the 18th IEEE International Conference on Pattern Recognition, volume 4, pages 53-56.
  8. Huang, C.-R., Chung, P.-C., Lin, K.-W., and Tseng, S.-C. (2010). Wheelchair detection using cascaded decision tree. IEEE Transactions on Information Technology in Biomedicine, 14(2):292-300.
  9. Myles, A., Lobo, N. D. V., and Shah, M. (2002). Wheelchair detection in a calibrated environment. In Proceedings of the 5th Asian Conference on Computer Vision, pages 706-712.
  10. Pan, J. and Hu, B. (2007). Robust occlusion handling in object tracking. In Proceedings of the 20th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1-8.
  11. Zhang, J., Presti, L. L., and Sclaroff, S. (2012). Online multi-person tracking by tracker hierarchy. In Proceedings of the 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, pages 379-385.
  12. Zhang, J., Presti, L. L., and (2013). Online multi-person tracker hierarchy. Available at: people.bu.edu/jmzhang/tracker hierarchy/ Tracker Hierarchy.htm [Accessed 14 Sept. 2016].
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Paper Citation


in Harvard Style

Tanikawa U., Kawanishi Y., Deguchi D., Ide I., Murase H. and Kawai R. (2017). Wheelchair-user Detection Combined with Parts-based Tracking . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 165-172. DOI: 10.5220/0006101101650172


in Bibtex Style

@conference{visapp17,
author={Ukyo Tanikawa and Yasutomo Kawanishi and Daisuke Deguchi and Ichiro Ide and Hiroshi Murase and Ryo Kawai},
title={Wheelchair-user Detection Combined with Parts-based Tracking},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={165-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006101101650172},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Wheelchair-user Detection Combined with Parts-based Tracking
SN - 978-989-758-226-4
AU - Tanikawa U.
AU - Kawanishi Y.
AU - Deguchi D.
AU - Ide I.
AU - Murase H.
AU - Kawai R.
PY - 2017
SP - 165
EP - 172
DO - 10.5220/0006101101650172