NDLI: Fast deformable structure regression tracking

Content Provider	IET Digital Library
Author	Yang, Xian Wang, Shoujue
Abstract	Visual object tracking is a challenging task because designing an effective and efficient appearance model is difficult. Current online tracking algorithms treat tracking as a classification task and use labelled samples to update appearance model. However, it is not clear to evaluate instance confidence belongs to the object. In this study, the authors propose a simple and efficient tracking algorithm with a deformable structure appearance. In their method, model updates with continuous labelled samples which are dense sampling. To improve the accuracy, they introduce a coupled-layer regression model which prevents negative background from impacting on the model learning rather than traditional classification. The proposed deformable structure regression tracker runs in real time and performs favourably against state-of-the-art trackers on various challenging sequences.
Starting Page	115
Ending Page	123
Page Count	9
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 2, Mar (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2014.0315
Journal	IET Computer Vision
Publisher Date	2016-03-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Task Computer Vision And Image Processing Technique Coupled-layer Regression Model Dense Sampling Effective Appearance Model Efficient Appearance Model Fast Deformable Structure Regression Tracking Image Classification Image Recognition Image Sampling Knowledge Engineering Technique Learning in AI Model Learning Negative Background Prevention Object Tracking Regression Analysis Statistics Visual Object Tracking
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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