Towards understanding socio-cognitive behaviors of crowds from visual surveillance data-Reference-Cited by-同舟云学术

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Towards understanding socio-cognitive behaviors of crowds from visual surveillance data

Published:2019-11-12 Issue:3-4 Volume:79 Page:1781-1799
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Zitouni M. Sami,Sluzek Andrzej,Bhaskar Harish

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/content/pdf/10.1007/s11042-019-08201-z.pdf

Reference39 articles.

1. Baig MW, Barakova EI, Marcenaro L, Regazzoni CS, Rauterberg M (2014) Bio-inspired probabilistic model for crowd emotion detection. In: International Joint Conference on Neural Networks (IJCNN), pp 3966–3973

2. Benfold B, Reid I (2011) Stable multi-target tracking in real-time surveillance video. In: CVPR, pp 3457–3464

3. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol 1, pp 886–893

4. Dehghan A, Idrees H, Zamir AR, Shah M (2014) Automatic detection and tracking of pedestrians in videos with various crowd densities. In: Pedestrian and Evacuation Dynamics 2012, Springer, pp 3–19

5. Dehghan A, Assari SM, Shah M (2015) Gmmcp tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4091–4099

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