Open AccessCollaborative model tracking with robust occlusion handling
Author(s) -
Kong Jun,
Ding Yitao,
Jiang Min,
Li Sha
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0827
Subject(s) - discriminative model , bittorrent tracker , artificial intelligence , classifier (uml) , computer science , eye tracking , occlusion , pattern recognition (psychology) , computer vision , mean shift , tracking (education) , medicine , psychology , pedagogy , cardiology
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.