Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

Motion Flow Segmentation Based on Optical Flow Angle

Authors
Kun Yang, Lin Wang, Fu-Jian Feng, Jiang-Hao Yu, Yuan-Fei Cheng
Corresponding Author
Kun Yang
Available Online December 2016.
DOI
https://doi.org/10.2991/icwcsn-16.2017.120How to use a DOI?
Keywords
Optical flow; Lyapunov exponent; Crowd target detection; Motion flow segmentation.
Abstract
Aiming at the irrationality of deep motion state analysis, such as density estimation, target tracking and behavior understanding, which taken the motion foreground as a whole in dense crowd scene. This paper proposes an algorithm to segment motion flow based on optical flow angle. First of all, the motion prospect is obtained by using the finite-time Lyapunov exponent (FTLE) algorithm. Then get the foreground optical flow information with the prospect as a mask. Finally, the dynamic K-means clustering method is applied to segment the foreground optical flow angle. Experimental results show that the proposed algorithm is clear and easy to implement, and the motion flow can be segmented accurately in different scenes.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
Part of series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-302-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icwcsn-16.2017.120How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Kun Yang
AU  - Lin Wang
AU  - Fu-Jian Feng
AU  - Jiang-Hao Yu
AU  - Yuan-Fei Cheng
PY  - 2016/12
DA  - 2016/12
TI  - Motion Flow Segmentation Based on Optical Flow Angle
BT  - 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.120
DO  - https://doi.org/10.2991/icwcsn-16.2017.120
ID  - Yang2016/12
ER  -