Robust Background Modeling with Kernel Density Estimation

Authors

  • Man Hua School of Computer Science , Civil Aviation Flight University of China
  • Yanling Li School of Computer Science , Civil Aviation Flight University of China
  • Yinhui Luo School of Computer Science , Civil Aviation Flight University of China

DOI:

https://doi.org/10.3991/ijoe.v11i8.4880

Keywords:

Adaptive, Background Modeling, Thresholding, Kernel Density Estimation.

Abstract


Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. In this paper, we proposed a novel adaptive approach modeling background and segmenting moving object with non-parametric kernel density estimation. Unlike previous approaches to object detection which detect objects by global threshold, we use a local threshold to reflect temporal persistence. With combined of global threshold and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.

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Published

2015-10-26

How to Cite

Hua, M., Li, Y., & Luo, Y. (2015). Robust Background Modeling with Kernel Density Estimation. International Journal of Online and Biomedical Engineering (iJOE), 11(8), pp. 13–16. https://doi.org/10.3991/ijoe.v11i8.4880