Abstract
Moving object detection play an important role in the application of computer vision. In recent years, the proposal and improvement of robust principal component analysis has broad application prospects in intelligent video surveillance and other fields. In order to enable domestic and foreign researchers to deeply explore and apply the RPCA, this paper systematically reviews it. This paper summarizes the latest research progress, summarizes various RPCA models at home and abroad, and theoretically analyses their advantages and disadvantages. In this paper, different improved models are applied to video sequences of different scenes, and comparative experiments are carried out. Overall, the current improved algorithms can effectively remedy the shortcomings of the original RPCA method and improve the accuracy of moving object detection. However, some limitations of RPCA need to be further studied.
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