Visual tracking based on stacked Denoising Autoencoder network with genetic algorithm optimization

Visual object tracking in dynamic environments with severe appearance variations is a significant problem in the computer vision field. This paper proposes a novel visual tracking algorithm that exploits the multiple level features learning ability of SDAE. There are two training stages for the SDAE...

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Bibliographic Details
Published in:	Multimedia tools and applications Vol. 77; no. 4; pp. 4253 - 4269
Main Authors:	Hua, Weixin, Mu, Dejun, Guo, Dawei, Liu, Hang
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2018 Springer Nature B.V
Subjects:	Back propagation Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Genetic algorithms Machine learning Multimedia Information Systems Neural networks Noise reduction Optical tracking Special Purpose and Application-Based Systems Training Tuning Sparsity dictionary encode Object tracking Stacked denoising autoencoder Genetic algorithm
ISSN:	1380-7501, 1573-7721
Online Access:	Get full text
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