Abstract
In this paper we address the problem of detecting reliably a subset of pedestrian targets (heads) in a high-density crowd exhibiting extreme clutter and homogeneity, with the purpose of obtaining tracking initializations. We investigate the solution provided by discriminative learning where we require that the detections in the image space be localized over most of the target area and temporally stable. The results of our tests show that discriminative learning strategies provide valuable cues about the target localization which may be combined with other complementary strategies in order to bootstrap tracking algorithms in these challenging environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ferryman, J., Ellis, A.L.: Performance evaluation of crowd image analysis using the PETS2009 dataset. Pattern Recogn. Lett. 44, 3–15 (2014). Pattern Recognition and Crowd Analysis
Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: an empirical study. Phys. Rev. E 75, 046109 (2007)
Krausz, B., Bauckhage, C.: Loveparade 2010: automatic video analysis of a crowd disaster. Comput. Vis. Image Underst. 116, 307–319 (2012)
Zhan, B., Monekosso, D., Remagnino, P., Velastin, S., Xu, L.Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19, 345–357 (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) - Volume 1, CVPR 2005, vol. 1, pp. 886–893. IEEE Computer Society, Washington, DC (2005)
Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, Proceedings, vol. 2, pp. II-459–466 (2003)
Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004, vol. 2, pp. 406–413. IEEE (2004)
Comaniciu, D., Meer, P., Member, S.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR, pp. 878–885 (2005)
Rabaud, V., Belongie, S.: Counting crowded moving objects. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 705–711 (2006)
Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: 19th International Conference on Pattern Recognition, 2008, ICPR 2008, pp. 1–4 (2008)
Li, M., Bao, S., Dong, W., Wang, Y., Su, Z.: Head-shoulder based gender recognition. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 2753–2756 (2013)
Ye, Q., Gu, R., Ji, Y.: Human detection based on motion object extraction and headshoulder feature. Optik - Int. J. Light Electron Opt. 124, 3880–3885 (2013)
Wang, S., Zhang, J., Miao, Z.: A new edge feature for head-shoulder detection. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 2822–2826 (2013)
Ali, S., Shah, M.: A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR 2007, pp. 1–6 (2007)
Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)
Moore, B.E., Ali, S., Mehran, R., Shah, M.: Visual crowd surveillance through a hydrodynamics lens. Commun. ACM 54, 64–73 (2011)
Idrees, H., Warner, N., Shah, M.: Tracking in dense crowds using prominence and neighborhood motion concurrence. Image Vis. Comput. 32, 14–26 (2014)
Aghajan, H., Cavallaro, A.: Multi-camera Networks: Principles and Applications. Academic Press, London (2009)
Javed, O., Shah, M.: Automated Multi-camera Surveillance: Algorithms and Practice. The International Series in Video Computing, vol. 10. Springer, New York (2008)
Eshel, R., Moses, Y.: Tracking in a dense crowd using multiple cameras. Int. J. Comput. Vis. 88, 129–143 (2010)
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34, 3–19 (2013)
Maji, S., Berg, A., 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)
Lin, H.T., Lin, C.J., Weng, R.: A note on platts probabilistic outputs for support vector machines. Mach. Learn. 68, 267–276 (2007)
Acknowledgement
K. Kiyani would like to acknowledge the Qatar QNRF under the grant NPRP 09-768-1-114.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Aldea, E., Marastoni, D., Kiyani, K.H. (2015). Spatio-Temporal Consistency for Head Detection in High-Density Scenes. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-16634-6_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16633-9
Online ISBN: 978-3-319-16634-6
eBook Packages: Computer ScienceComputer Science (R0)