Moving Target Detection Based on an Adaptive Low-Rank Sparse Decomposition


  • Jiang Chong School of Information Science and Engineering, Hunan Women's University, 410004 Changsha, China



Detection of moving objects, low-rank, sparse decomposition, adaptive robust, principal component analysis


For the exact detection of moving targets in video processing, an adaptive low-rank sparse decomposition algorithm is proposed in this paper. In the paper's algorithm, the background model and the solved frame vector are first used to construct an augmented matrix, then robust principal component analysis (RPCA) is used to perform a low-rank sparse decomposition on the enhanced augmented matrix. The separated low-rank part and sparse noise correspond to the background and motion foreground of the video frame, respectively, the incremental singular value decomposition method and the current background vector are used to update the background model. The experimental results show that the algorithm can deal with complex scenes such as light changes and background motion better, and the algorithm's delay and memory consumption can be reduced effectively.


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How to Cite

Chong, J. (2021). Moving Target Detection Based on an Adaptive Low-Rank Sparse Decomposition. COMPUTING AND INFORMATICS, 39(5), 1061–1081.