Action Recognition Using Low-Rank Sparse Representation-Reference-Cited by-同舟云学术

Action Recognition Using Low-Rank Sparse Representation

Published:2018 Issue:3 Volume:E101.D Page:830-834
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

CHENG Shilei¹,GU Song²,YE Maoquan¹,XIE Mei¹

Affiliation:

1. School of Electronic Engineering, University of Electronic Science and Technology of China

2. Department of Aircraft Maintenance Engineering, Chengdu Aeronautic Polytechnic

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E101.D/3/E101.D_2017EDL8176/_pdf

Reference24 articles.

1. [1] R. Poppe, “A survey on vision-based human action recognition,” Image and Vision Computing, vol.28, no.6, pp.976-990, 2010. 10.1016/j.imavis.2009.11.014

2. [2] C. Deng, X. Cao, H. Liu, and J. Chen, “A global spatio-temporal representation for action recognition,” 2010 20th International Conference on Pattern Recognition (ICPR), pp.1816-1819, IEEE, 2010. 10.1109/icpr.2010.448

3. [3] P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features,” 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp.65-72, IEEE, 2005. 10.1109/vspets.2005.1570899

4. [4] F. Liu, J. Tang, Y. Song, X. Xiang, and Z. Tang, “Local structure based sparse representation for face recognition with single sample per person,” 2014 IEEE International Conference on Image Processing (ICIP), pp.713-717, IEEE, 2014. 10.1109/icip.2014.7025143

5. [5] G. Liu, Z. Lin, and Y. Yu, “Robust subspace segmentation by low-rank representation,” Proc. 27th International Conference on Machine Learning (ICML-10), pp.663-670, 2010.

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