Corpus ID: 219686867

Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection

@article{Shao2020HyperRJ,
  title={Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection},
  author={Zerui Shao and Y. Pu and J. Zhou and B. Wen and Y. Zhang},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.07795}
}
  • Zerui Shao, Y. Pu, +2 authors Y. Zhang
  • Published 2020
  • Computer Science
  • ArXiv
  • Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporallyvarying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be lowrank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 60 REFERENCES
    Robust Online Matrix Factorization for Dynamic Background Subtraction
    77
    Robust Principal Component Analysis Based on Maximum Correntropy Criterion
    219
    Image quality assessment: from error visibility to structural similarity
    23084