Open AccessVisual Tracking Algorithm Based on Color Name Histogram
Author(s) -
Yue Chenchen,
Wei Yu,
Zhiqiang Hou,
Songde Ma
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012063
Subject(s) - histogram , artificial intelligence , color histogram , pixel , color normalization , computer vision , rgb color model , feature (linguistics) , robustness (evolution) , computer science , histogram matching , pattern recognition (psychology) , color space , algorithm , mathematics , color image , image (mathematics) , image processing , linguistics , philosophy , biochemistry , chemistry , gene
Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.