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Proceedings Paper

An optical flow network for enhancing the edge information
Author(s): Wanqi Sun; Xinzhu Sang; Duo Chen; Peng Wang; Huachun Wang
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Paper Abstract

The deep convolution neural network has been widely tackled for optical flow estimation in recent works. Due to advantages of extracting abstract features and efficiency, the accuracy of optical flow estimation using CNN is improved steadily. However, the edge information for most flow predictions is vague. Here, two methods are presented to add extra useful information in training our optical flow network, for the purpose of enhancing edge information of the result. The edges map is added into the input section, and the motion boundary is considered for the input section. Experimental result shows that the accuracy with both methods is higher than the control experiment. 3.71% and 7.54% are improved by comparing just a pair of frames in the input section respectively.

Paper Details

Date Published: 2 November 2018
PDF: 9 pages
Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 108170T (2 November 2018); doi: 10.1117/12.2500540
Show Author Affiliations
Wanqi Sun, Beijing Univ. of Posts and Telecommunications (China)
Xinzhu Sang, Beijing Univ. of Posts and Telecommunications (China)
Duo Chen, Beijing Univ. of Posts and Telecommunications (China)
Peng Wang, Beijing Univ. of Posts and Telecommunications (China)
Huachun Wang, Beijing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 10817:
Optoelectronic Imaging and Multimedia Technology V
Qionghai Dai; Tsutomu Shimura, Editor(s)

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