NDLI: Robust part-based visual tracking via adaptive collaborative modelling

Content Provider	IET Digital Library
Author	Kong, Jun Wang, Benxuan Jiang, Min
Abstract	Discriminative correlation filter-based tracking algorithms have recently shown impressive performance on benchmark data sets. However, visual tracking is still a challenging task in the case of partial occlusions, irregular deformations and so on. In this study, the authors intend to solve these issues by introducing the adaptive collaborative model into part-based tracking. First, instead of a simple linear superposition, the collaborative strategy they proposed combines the template model and colour-based model adaptively and relies on the strengths of both to promote the accuracy. Second, we utilise the voting strategy to figure out the final object position from reliable parts, and the motion information is used in evaluation for reliable parts to enable the tracker to be robust in various situations. Third, the authors utilise a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, they introduce a dimensionality reduction method to limit the computational complexity of the tracker. Abundant experiments demonstrate that the tracker performs superiorly against several advanced algorithms on both the Online Tracking Benchmark (OTB) 2013 and OTB2015 data sets while maintaining the high frame rates.
Starting Page	1648
Ending Page	1657
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6027
Journal	IET Image Processing
Publisher Date	2019-05-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Collaborative Model Adaptive Collaborative Modelling Benchmark Data Sets Collaborative Strategy Colour-based Model Computer Vision Computer Vision And Image Processing Technique Discriminative Correlation Filter-based Tracking Algorithm Discriminative Multiscale Estimate Method Feature Extraction Filtering Method in Signal Processing Filtering Theory Final Object Position Image Colour Analysis Image Representation Image Sequence Impressive Performance Irregular Deformations Knowledge Engineering Technique Learning in AI Object Detection Object Tracking Online Tracking Benchmark 2013 Optical, Image And Video Signal Processing OTB2015 Data Sets Part-based Tracking Partial Occlusions Reliable Parts Robust Part-based Visual Tracking Simple Linear Superposition Template Model Tracker Performs Voting Strategy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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