Research Article
Surveillance Video Denoising Based on Background Modeling
@INPROCEEDINGS{10.1109/CHINACOM.2008.4685224, author={Yunhai Liu and Baolei Xi and Heyi Guo and Xiaochen Quan and Shengtian Yang}, title={Surveillance Video Denoising Based on Background Modeling}, proceedings={ChinaCom2008-Signal Processing for Communications Symposium}, publisher={IEEE}, proceedings_a={CHINACOM2008-SPC}, year={2008}, month={11}, keywords={Surveillance video denoising; segmentation; background models; 3D median filter}, doi={10.1109/CHINACOM.2008.4685224} }- Yunhai Liu
Baolei Xi
Heyi Guo
Xiaochen Quan
Shengtian Yang
Year: 2008
Surveillance Video Denoising Based on Background Modeling
CHINACOM2008-SPC
IEEE
DOI: 10.1109/CHINACOM.2008.4685224
Abstract
Because of the characteristics of photoelectric sensors and the working environment of cameras, real-time surveillance video contains much noise, which does not only decrease the subjective visual quality, but also increases the output bitrate of video encoder. The effect of partial spatio-temporal smoothing is not evident. According to the characteristics of surveillance video, we propose a novel algorithm based on video content, setting up adaptive background models to accomplish foreground segmentation, reducing background noise via model parameters and foreground noise via 3D median filter. To the sequences of “hall_monitor” polluted with Gaussian or Poisson noise, the results show that the new algorithm increases PSNR about 8dB, and saves over 90% of encoder output bitrate.
