Open AccessAn Improved TLD Target Tracking Algorithm Combined with Kernel Correlation Filtering
Author(s) -
Haoyu Duan,
Yumin Zhang,
Sheng Wei,
Xin Chen,
Jianxin Ren
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/440/3/032015
Subject(s) - tracking (education) , computer science , kernel (algebra) , artificial intelligence , computer vision , correlation , tracking system , eye tracking , algorithm , mathematics , filter (signal processing) , psychology , pedagogy , geometry , combinatorics
Target continuous tracking problem is an important research topic in the field of computer vision. To conquer difficulties of target loss under complex situation, size changes and illumination changes, an improved tracking module is designed via combining tracking module in TLD with KCF (Kernelized Correlation Filters). The comparison between TLD and the proposed algorithm shows that the performance of the latter is improved in tracking accuracy. Under complex situation of target occlusion, scale change, illumination change, etc., algorithm presented can output the tracking results stably, which is more robust and more suitable for long-term target tracking.