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A method of extracting target trajectory by deep convolution network in infrared images
Author(s): Tianwei Yang; Jungang Yang; Yang Sun; Wei An; Jing Wu
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Paper Abstract

The fully convolution network is a very powerful visual model that can be used to extract features in an image. We improved a network model that can be used for end-to-end, pixel-to-pixel training to extract target motion trajectories in infrared images. The dataset used in our training comes from the simulation dataset produced by the public infrared dataset combined with the simulation trajectory. In order to enhance the model’s robustness, we add the pepper and salt noise and white noise to the simulated image, and use image augmentation to increase the number of the image. We achieved highly train and test accuracy in our simulation dataset.

Paper Details

Date Published: 3 January 2020
PDF: 5 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137310 (3 January 2020); doi: 10.1117/12.2557617
Show Author Affiliations
Tianwei Yang, National Univ. of Defense Technology (China)
Jungang Yang, National Univ. of Defense Technology (China)
Yang Sun, National Univ. of Defense Technology (China)
Wei An, National Univ. of Defense Technology (China)
Jing Wu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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