Mobile Camera Background Subtraction Based on Graph-Based Semi-Supervised Learning
When performing background subtraction on videos captured by mobile cameras,existing unsupervised and supervised learning models have poor generalization ability.To solve this problem,a background subtraction model for a moving camera is proposed based on graph representation and semi-supervised learning.Firstly,a semi-supervised learning model based on the convex non-convex graph total variance regularization is proposed.This model constructed a non-convex graph total variance regularization by using the difference between the L1 norm and its gen-eralized Moreau envelope,which can avoid the biased estimation caused by the L1 regularization.The global convexity of the objective function under certain condition was also theoretically proved.Furthermore,the alternating directional multiplier method algorithm was used to solve the proposed model.In numerical experiments,the proposed model was applied to background subtraction,and the comparison experiments were conducted on the PTZ challenge of CD-net2014 dataset.The experimental results show that the proposed model outperforms the existing unsupervised and su-pervised learning models in both visual effects and numerical criteria for background subtraction for a moving camera.
Background subtractionSemi-supervised learningGraph representationConvex non-convex total varianceAlternation directional multiplier method
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