Corpus ID: 202661049

Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection

@article{KhalilianGourtani2019MaskedRPCASA,
  title={Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection},
  author={Amirhossein Khalilian-Gourtani and Shervin Minaee and Yao Wang},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.08049}
}
  • Amirhossein Khalilian-Gourtani, Shervin Minaee, Yao Wang
  • Published in ArXiv 2019
  • Computer Science, Engineering
  • Foreground detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance system. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition and accomplishes such a task when the background is stationary and the foreground is dynamic and relatively small. A fundamental issue with RPCA is the assumption that the low-rank and sparse components are added at each element, whereas in reality, the moving foreground is… CONTINUE READING

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