Moving Target Detection Based on l 1/22-TV Regularization RPCA
Focusing on the issue that the accuracy of moving target detection decreases due to background inter-ference in complex environment,a moving target detection method based on l1/2-TV regularized RPCA is proposed.The method represents video sequences as three components,i.e.,a low-rank background,a moving target,and noise.The algorithm uses the kernel norm to describe the low rank characteristics of the background.The l1/2 norm is used to describe the more sparse moving target to suppress the background interference in the moving target.At the same time,TV regularization is combined to constrain the spatial continuity of the moving target to make the moving target more complete.Frobenius norm is used to detect background interference.The augmented Lagrangian multiplier meth-od extended by the alternating direction minimization strategy is used to deal with the proposed constraint minimization problem.The experimental results show that the proposed method can effectively remove the background interference,improve the detection accuracy of moving targets and improve the visual effect.
Moving object detectionComplex environmentRobust principal component analysisTotal variation
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