NDLI: Real-time least-squares ensemble visual tracking

Content Provider	IET Digital Library
Author	Zhu, Ridong Yang, Xiaoyuan Wang, Jingkai Li, Zhengze
Abstract	In this study, the authors present a novel ensemble tracking system by formulating the tracking task in terms of a linear regression which is a least-squares problem. A set of weak classifiers are trained using least squares which are solved efficiently using the Moore–Penrose inverse. Then, these weak classifiers are combined into a strong classifier using bagging. The strong classifier is used to recognise the target and locate its position, which is obtained efficiently in the Fourier domain. For obtaining a good ensemble, a novel sampling strategy is proposed to train accurate and diverse weak classifiers. By exploiting historical targets to monitor the training process, pose change and occlusion are well-handled. The proposed method is extensively evaluated using a variety of evaluation protocols on the recent standard datasets including OTB50, OTB100 and VOT2016. Experimental results show that the proposed methodology performs favourably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
Starting Page	53
Ending Page	61
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6037
Journal	IET Image Processing
Publisher Date	2019-10-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Diverse Weak Classifier Fourier Domain Historical Targets Image Classification Image Recognition Image Sampling Knowledge Engineering Technique Learning in AI Least Squares Approximation Least-squares Problem Linear Regression Moore–Penrose Inverse Novel Ensemble Tracking System Object Tracking Regression Analysis Statistics Strong Classifier Tracking Task Training 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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