NDLI: Regularisation learning of correlation filters for robust visual tracking

Content Provider	IET Digital Library
Author	Jiang, Min Shen, Jianyu Kong, Jun Huo, Hongtao
Abstract	Recently, kernelised correlation filter (KCF)-based trackers aroused increasing interest and achieved extremely compelling results in different competitions and benchmarks in the field of visual object tracking. However, the training mechanism of the KCF that exploits simple linear combinations of filter from the previous frame easily cause error accumulation. To overcome this problem, the authors propose a novel training strategy that utilises all of the previous training samples, and a sparsity-related loss function regularised by the L1 norm to deal with the problem of the fixed template size in KCF trackers, a separate scale filter is learned for scale estimation during the tracking process. Moreover, powerful features that include histogram of oriented gradients (HOG) and colour features are integrated to further improve the robustness of the authors’ tracking. Extensive experiments in various challenging situations demonstrate that the proposed method performs favourably against several state-of-the-art tracking algorithms.
Starting Page	1586
Ending Page	1594
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 9, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1043
Journal	IET Image Processing
Publisher Date	2018-04-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Author Tracking Colour Features Computer Vision And Image Processing Technique Correlation Filter Error Accumulation Feature Extraction Filtering Method in Signal Processing Fixed Template Size Histogram of Oriented Gradients HOG Image Colour Analysis Image Filtering Image Recognition KCF Trackers Kernelised Correlation Filter L1 Norm Regularization Learning in AI Neural Computing Technique Object Detection Object Tracking Regularisation Learning Robust Visual Tracking Algorithm Scale Estimation Separate Scale Filter Sparsity-related Loss Function Target Tracking Tracking Process Training Mechanism Training Strategy Visual Object Tracking
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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