Open AccessScale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter
Author(s) -
Ting Zhang,
Dong Hu,
Jing Zhang
Publication year - 2021
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 - 10.1088/1742-6596/1828/1/012042
Subject(s) - kernel (algebra) , block (permutation group theory) , artificial intelligence , computer science , computer vision , tracking (education) , scale (ratio) , position (finance) , filter (signal processing) , frame (networking) , convolution (computer science) , transformation (genetics) , pattern recognition (psychology) , correlation coefficient , mathematics , artificial neural network , machine learning , telecommunications , geography , psychology , pedagogy , geometry , cartography , finance , combinatorics , economics , biochemistry , chemistry , gene
Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.