Tracking people in RGBD videos using deep learning and motion clues-Reference-Cited by-同舟云学术

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Tracking people in RGBD videos using deep learning and motion clues

Published:2016-09 Issue: Volume:204 Page:70-76
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Xue Hongyang,Liu Yao,Cai Deng,He Xiaofei

Funder

National Program for Special Support of Top-Notch Young Professionals

National Natural Science Foundation of China

National Basic Research Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference26 articles.

1. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, IEEE, San Diego, 2005, pp. 886–893.

2. X. Cui, Y. Liu, S. Shan, X. Chen, W. Gao, 3d Haar-like features for pedestrian detection, in: 2007 IEEE International Conference on Multimedia and Expo, IEEE, Beijing, 2007, pp. 1263–1266.

3. A large margin framework for single camera offline tracking with hybrid cues;Khanloo;Comput. Vis. Image Underst.,2012

4. B.Y.S. Khanloo, F. Stefanus, M. Ranjbar, Z.-N. Li, N. Saunier, T. Sayed, G. Mori, Max-margin offline pedestrian tracking with multiple cues, in: 2010 Canadian Conference on Computer and Robot Vision (CRV), IEEE, Ottawa, 2010, pp. 347–353.

5. M2tracker;Mittal;Int. J. Comput. Vis.,2003

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