Detecting People in Large Crowded Spaces using 3D Data from Multiple Cameras

João Carvalho, Manuel Marques, João Paulo Costeira, Pedro Mendes Jorge

2016

Abstract

Real time monitoring of large infrastructures has human detection as a core task. Since the people anonymity is a hard constraint in these scenarios, video cameras can not be used. This paper presents a low cost solution for real time people detection in large crowded environments using multiple depth cameras. In order to detect people, binary classifiers (person/notperson) were proposed based on different sets of features. It is shown that good classification performance can be achieved choosing a small set of simple feature.

References

  1. Arras, K. O., Mozos, O. M., and Burgard, W. (2007). Using boosted features for the detection of people in 2d range data. In IEEE ICRA, pages 3402-3407. IEEE.
  2. Bondi, E., Seidenari, L., Bagdanov, A. D., and Del Bimbo, A. (2014). Real-time people counting from depth imagery of crowded environments. In Advanced Video and Signal Based Surveillance (AVSS), 11th IEEE International Conference on, pages 337-342. IEEE.
  3. Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
  4. Brscic, D., Kanda, T., Ikeda, T., and Miyashita, T. (2013). Person tracking in large public spaces using 3-d range sensors. Human-Machine Systems, IEEE Transactions on, 43(6):522-534.
  5. Choi, B., Meric¸li, C., Biswas, J., and Veloso, M. (2013). Fast human detection for indoor mobile robots using depth images. In IEEE ICRA, pages 1108-1113. IEEE.
  6. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273-297.
  7. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE CVPR Computer Society Conference on, volume 1, pages 886- 893. IEEE.
  8. Ess, A., Leibe, B., Schindler, K., and Van Gool, L. (2009). Moving obstacle detection in highly dynamic scenes. In IEEE ICRA, pages 56-63. IEEE.
  9. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning.
  10. Hegger, F., Hochgeschwender, N., Kraetzschmar, G. K., and Ploeger, P. G. (2013). People detection in 3d point clouds using local surface normals. In RoboCup 2012: Robot Soccer World Cup XVI, pages 154-165. Springer.
  11. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning. Springer.
  12. Lin, W.-C., Sun, S.-W., and Cheng, W.-H. (2013). Demo paper: A depth-based crowded heads detection system through a freely-located camera. In IEEE ICME Workshops, pages 1-2. IEEE.
  13. Liu, J., Liu, Y., Zhang, G., Zhu, P., and Chen, Y. Q. (2015). Detecting and tracking people in real time with rgb-d camera. Pattern Recognition Letters, 53:16-23.
  14. Mikolajczyk, K., Schmid, C., and Zisserman, A. (2004). Human detection based on a probabilistic assembly of robust part detectors. In Computer Vision-ECCV, pages 69-82. Springer.
  15. Mitzel, D. and Leibe, B. (2011). Real-time multi-person tracking with detector assisted structure propagation. In IEEE ICCV Workshops, pages 974-981. IEEE.
  16. Moeslund, T. B., Hilton, A., and Krüger, V. (2006). A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding, 104(2):90-126.
  17. Munaro, M., Basso, F., and Menegatti, E. (2012). Tracking people within groups with rgb-d data. In IEEE/RSJ IROS International Conference on, pages 2101-2107. IEEE.
  18. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
  19. Rusu, R. B. and Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). In IEEE ICRA, Shanghai, China.
  20. Spinello, L. and Arras, K. O. (2011). People detection in rgb-d data. In IEEE/RSJ IROS International Conference on, pages 3838-3843. IEEE.
  21. Wohlkinger, W. and Vincze, M. (2011). Ensemble of shape functions for 3d object classification. In IEEE ROBIO International Conference on, pages 2987-2992. IEEE.
  22. Xia, L., Chen, C.-C., and Aggarwal, J. K. (2011). Human detection using depth information by kinect. In IEEE CVPR Workshops Computer Society Conference on, pages 15-22. IEEE.
  23. Zhu, Q., Yeh, M.-C., Cheng, K.-T., and Avidan, S. (2006). Fast human detection using a cascade of histograms of oriented gradients. In IEEE CVPR Computer Society Conference on, volume 2, pages 1491-1498. IEEE.
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Paper Citation


in Harvard Style

Carvalho J., Marques M., Costeira J. and Jorge P. (2016). Detecting People in Large Crowded Spaces using 3D Data from Multiple Cameras . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 218-225. DOI: 10.5220/0005727702180225


in Bibtex Style

@conference{visapp16,
author={João Carvalho and Manuel Marques and João Paulo Costeira and Pedro Mendes Jorge},
title={Detecting People in Large Crowded Spaces using 3D Data from Multiple Cameras},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={218-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005727702180225},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Detecting People in Large Crowded Spaces using 3D Data from Multiple Cameras
SN - 978-989-758-175-5
AU - Carvalho J.
AU - Marques M.
AU - Costeira J.
AU - Jorge P.
PY - 2016
SP - 218
EP - 225
DO - 10.5220/0005727702180225