NDLI: Collaborative model tracking with robust occlusion handling

Content Provider	IET Digital Library
Author	Kong, Jun Ding, Yitao Jiang, Min Li, Sha
Abstract	Currently, the discriminative correlation filter-based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier-box. Firstly, they introduce complex colour features into correlation filter tracker to improve the effect of the tracker. Secondly, they introduce a multi-scale method into their tracker to ease the scale problem. Thirdly, in order to solve the heavy occlusion in the tracking process, they adopt the locally weighted distance and classifier-box. Their algorithm achieves distance precision rates of 81.7 and 77.4% on OTB2013 dataset and OTB2015 dataset, respectively. Their contribution focuses on solving heavy occlusion by using colour features, locally weighted distance and classifier-box. The experimental results on OTB2013 and OTB2015 datasets demonstrate their algorithm to perform better than state-of-the-art methods.
Starting Page	1701
Ending Page	1709
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0827
Journal	IET Image Processing
Publisher Date	2020-01-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classifier-box Collaborative Model Complex Colour Features Computer Vision And Image Processing Technique Correlation Filter Tracker Discriminative Correlation Filter-based Trackers Feature Extraction Filtering Method in Signal Processing Filtering Theory Heavy Occlusion Higher Tracking Accuracy Image Colour Analysis Image Motion Analysis Locally Weighted Distance Multiscale Method Object Detection Object Tracking Optical, Image And Video Signal Processing OTB2013 Dataset OTB2015 Dataset Robust Occlusion Handling Scale Problem Scale Variation Target Tracking Tracking Process Visual Tracking
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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