CLASS: Collaborative Low-Rank and Sparse Separation for Moving Object Detection

@article{Zheng2017CLASSCL,
  title={CLASS: Collaborative Low-Rank and Sparse Separation for Moving Object Detection},
  author={Aihua Zheng and Minghe Xu and Bin Luo and Zhili Zhou and Chenglong Li},
  journal={Cognitive Computation},
  year={2017},
  volume={9},
  pages={180-193}
}
  • Aihua Zheng, Minghe Xu, +2 authors Chenglong Li
  • Published in Cognitive Computation 2017
  • Computer Science
  • Low-rank models have been successfully applied to background modeling and achieved promising results on moving object detection. However, the assumption that moving objects are modelled as sparse outliers limits the performance of these models when the sizes of moving objects are relatively large. Meanwhile, inspired by the visual system of human brain which can cognitively perceive the physical size of the object with different sizes of retina imaging, we propose a novel approach, called… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 46 REFERENCES

    Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    Robust principal component analysis?

    VIEW 9 EXCERPTS
    HIGHLY INFLUENTIAL

    Cross-based local multipoint filtering

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    ViBe: A Universal Background Subtraction Algorithm for Video Sequences

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    Adaptive background mixture models for real-time tracking

    • Chris Stauffer, W. Eric L. Grimson
    • Computer Science
    • Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)
    • 1999
    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Learning Sampling Distributions for Efficient Object Detection

    VIEW 1 EXCERPT

    Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection

    An Approach to Streaming Video Segmentation With Sub-Optimal Low-Rank Decomposition

    VIEW 1 EXCERPT