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Target Tracking System Constructed by ELM-AE and Transfer Representation Learning

Jisuanji kexue yu tansuo, 2022-07, Vol.16 (7), p.1633-1648 [Peer Reviewed Journal]

ISSN: 1673-9418 ;DOI: 10.3778/j.issn.1673-9418.2012028

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  • Title:
    Target Tracking System Constructed by ELM-AE and Transfer Representation Learning
  • Author: YANG Zheng, DENG Zhaohong, LUO Xiaoqing, GU Xin, WANG Shitong
  • Subjects: extreme learning machine (elm)|extreme learning machine autoencoder (elm-ae)|transfer represen-tation learning (trl)|feature adaptation|gaussian naive bayes classifier (gnbc)|object tracking
  • Is Part Of: Jisuanji kexue yu tansuo, 2022-07, Vol.16 (7), p.1633-1648
  • Description: In the target tracking algorithm, the feature model's ability to quickly learn image features and the ability to adapt to changes in target features during tracking has always been one of the main research directions of target tracking algorithms. Especially for discriminative target trackers based on image block learning, these two points have become decisive factors affecting the efficiency and robustness of the tracker. However, the performance of most existing similar algorithms on these two abilities cannot achieve satisfactory results. To solve this problem, an efficient and robust feature model is proposed. The feature model first uses extreme learning machine autoencoder (ELM-AE) to quickly perform random feature mapping on complex image features of the target and background image blocks, and then uses the transfer learning ability of transfer representation learning (TRL) to improve the adaptability of random feature space. The feature model is named transfer representation learning with ELM-AE (TRL-
  • Publisher: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
  • Language: Chinese
  • Identifier: ISSN: 1673-9418
    DOI: 10.3778/j.issn.1673-9418.2012028
  • Source: DOAJ Directory of Open Access Journals
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