Open AccessMulti-scale Correlation Filter Tracking Algorithm Based on Feature Fusion
Author(s) -
Xiaozhen Ren,
Hongxiang Wang,
Yongye Wang,
Xingzhen Li,
Xingxing Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012114
Subject(s) - feature (linguistics) , artificial intelligence , tracking (education) , scale (ratio) , histogram , filter (signal processing) , pattern recognition (psychology) , computer vision , fusion , algorithm , computer science , kernel (algebra) , position (finance) , mathematics , image (mathematics) , psychology , pedagogy , philosophy , linguistics , physics , quantum mechanics , combinatorics , finance , economics
For the Kernel Correlation Filter (KCF) algorithm using a single feature can not fully describe the tracking target, and is susceptible to the change of the target scale, lacking the estimation of the target scale size, an improved feature fusion and multi-scale correlation filter tracking algorithm based on KCF is proposed. Firstly, in the stage of location prediction, color features and directional gradient histogram hog features are fused by feature series, and feature dimensions are added to realize target location prediction. Then the scale filter is introduced into the predicted target position to estimate the optimal target scale as the tracking result. Experimental results show that the accuracy and success rate of the improved algorithm are significantly improved compared with other classical tracking algorithms, and it can deal with scale change, occlusion, deformation and other complex situations robustly.