Human Action Recognition Algorithm Based on Improved Dense Trajectories

DOI

10.2991/icmmita-16.2016.111How to use a DOI?

Keywords

Action recognition; Improved dense trajectories; Camera motion elimination

Abstract

Objective Human action recognition is a hot topic in computer vision. The recognition of human actions from unconstrained videos is difficult because of complex background, illumination variation and camera motion. An improved dense trajectory-based approach is proposed to address such problem. Dense optical flow is utilized to track the scale invariant feature transform keypoints at multiple spatial scales. The histogram of oriented gradient,histogram of optical flow,and motion boundary histogram are employed to depict the trajectory efficiently. To eliminate the influence of camera motions, the consistence indirection is used to improve the robustness of trajectory. The Fisher vector model is utilized to compute one Fisher vector for each descriptor separately, and then the linear support vector machine is employed for classification.Experimental results on KTH and YouTube datasets demonstrate that the proposed algorithm can effectively recognize human actions.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yuling Sun
AU  - Peng Gan
AU  - Xiao Yu
PY  - 2017/01
DA  - 2017/01
TI  - Human Action Recognition Algorithm Based on Improved Dense Trajectories
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 595
EP  - 601
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.111
DO  - 10.2991/icmmita-16.2016.111
ID  - Sun2017/01
ER  -