Joint fuzzy background and adaptive foreground model for moving target detection |
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| Authors: | Dawei ZHANG Peng WANG Yongfeng DONG Linhao LI Xin LI |
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| Affiliation: | 1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China2. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6109, USA |
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| Abstract: | Moving target detection is one of the most basic tasks in computer vision. In conventional wisdom, the problem is solved by iterative optimization under either Matrix Decomposition (MD) or Matrix Factorization (MF) framework. MD utilizes foreground information to facilitate background recovery. MF uses noise-based weights to fine-tune the background. So both noise and foreground information contribute to the recovery of the background. To jointly exploit their advantages, inspired by two framework complementary characteristics, we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization (JMDF). To improve background extraction, a fuzzy factorization is designed. The fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background estimation. To describe the spatio-temporal continuity of foreground more accurately, we propose to incorporate the first order temporal difference into the group sparsity constraint adaptively. The temporal constraint is adjusted adaptively. Both foreground and the background are jointly estimated through an effective alternate optimization process, and the noise can be modeled with the specific probability distribution. The experimental results of vast real videos illustrate the effectiveness of our method. Compared with the current state-of-the-art technology, our method can usually form the clearer background and extract the more accurate foreground. Anti-noise experiments show the noise robustness of our method. |
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| Keywords: | matrix decomposition matrix factorization generalized sparsity noise modeling |
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