融合l1-TV正则化约束RPCA模型的视频去噪和目标检测 下载: 882次
detection in complex environment is affected by many factors. Traditional robust principal component analysis (RPCA) fails to obtain the lowest rank representation from disturbed data. Therefore, a novel method of video denoising and object detection algorithm based on RPCA model with l1-total variational (TV) regularization constraints is proposed. Based on RPCA, under the framework of low-rank sparse decomposition, the low-rank nature of the nuclear norm is used to model the background, and the three-dimensional TV combined with l1 norm regularization to constrain the sparsity and spatial continuity of the foreground object, and then l2 norm regularization is combined to constrain the noise part so as to make up for the deficiencies of the existing RPCA model. Using alternating iteration method, augmented Lagrange multiplier method is used to optimize the objective function, and the denoising and target detection in complex environment are realized. Experimental results show that the method can not only accurately detect moving objects under noise interference, but also maintain a relatively fast running speed, which provides a reference for the real-time detection of video. Compared with other similar methods, it not only has better detection effect, but also has advantages in the three indicators of quantitative evaluation.
杨国亮, 喻丁玲, 赖振东. 融合l1-TV正则化约束RPCA模型的视频去噪和目标检测[J]. 激光与光电子学进展, 2020, 57(16): 161506. Guoliang Yang, Dingling Yu, Zhendong Lai. Video Denoising and Object Detection Based on RPCA Model with l1-TV Regularization Constraints[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161506.