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Overview: In the field of image processing, the way of exacting image feature has always been one of the fundamental tasks. Different image descriptions will affect the performance of tracking algorithm directly. There are so many domestic and international researchers proposed classical image features, which can be sorted as two class: 1) based on deep learning, which have gained excellent results, including VGGNet and DenseNet, but it needs a large number of data to train the model and has several restrictions on the experimental platform; 2) based on manual features, which can be took on any platform in exit and also have obtained remarkable performance in image processing, including scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), and color name (CN). So, making a profound study on manual features is crucial. At present, several mainstream algorithms using CN all adopt principal component analysis (PCA) to process the feature. However, PCA assumes that the noise of input data must obey Gaussian distribution, which is a conspicuous defect. Aim to address this problem, in this paper, we take robust principal component analysis (Robust PCA) to process CN features. The method projects the original RGB color space to a robust color space–CN space, which means that the input image is layered to 11 layers according to CN feature. Then, it processes the CN features by the Robust PCA, so that the mapped image information is concentrated on a few layers, retaining a great quantity of image information and filting out noise. The processed feature is used for Color-name tracking frame at the standard benchmark OTB100, with mainly 11 challenges (e.g., occlusion, deformation). We set up different layers to compare the performance differences of the algorithm. The experimental results show that the success rate increases by 1.0% and the accuracy increases by 0.9% at OTB100. Compared with other classical algorithms, this way shows better robust and distinguishability of feature on visual tracking in most cases. Therefore, using Robust PCA to process CN feature can be significantly applied to other image processing applications. However, this way still has shortages, such as filtering the noise of target not completely in the visual tracking process. In follow-up work, we will further optimize the feature with different ways and try our best to combine the processed feature with deep learning-features to obtain excellent features in visual tracking and to remain applicable to other image processing applications simultaneously.
The original CN feature. (a0) Original image with salt & pepper noise; (a1)~(a11) Original CN features
The feature processed by PCA. (a0) Image with salt & pepper noise; (a1)~(a11) Features processed by PCA
The color name feature processed by Robust PCA. (a0) Original image with salt & pepper noise; (a0)~(a11) CN features processed by Robust PCA; (b1)~(b11) The noise of processed by CN features
Results of visual tracking. (a) Bolt; (b) Tiger1; (c) Jogging-1; (d) Soccer; (e) Freeman4; (f) David3
Precision (a) and success (b) plots
Evaluation results based on attribute. (a) Illumination variation; (b) Occlusion; (c) Motion blur
Results of visual tracking. (a) Girl; (b) Jumping; (c) Couple; (d) Freeman4; (e) Jogging-1; (f) Singer2
Result compare the performance differences of the algorithm at the standard benchmark OTB100. (a) Precision; (b) Success rate
Evaluation attribute results CN features processed based on Robust PCA applied to SAMF algorithms.