Adaptive Background Defogging with Foreground Decremental Preconditioned Conjugate Gradient
The quality of outdoor surveillance videos are always degraded by bad weathers, such as fog, haze, and snowing. The degraded videos not only provide poor visualizations, but also increase the difficulty of vision-based analysis such as foreground/background segmentation. However, haze/fog removal has never been an easy task, and is often very time consuming. Most of the existing methods only consider a single image, and no temporal information of a video is used. In this paper, a novel adaptive background defogging method is presented. It is observed that most of the background regions between two consecutive video frames do not vary too much. Based on this observation, each video frame is firstly defogged by a background transmission map which is generated adaptively by the proposed foreground decremental preconditioned conjugate gradient (FDPCG). It is shown that foreground/background segmentation can be improved dramatically with such background-defogged video frames. With the help of a foreground map, the defogging of foreground regions is then completed by 1) foreground transmission estimation by fusion, and 2) transmission refinement by the proposed foreground incremental preconditioned conjugate gradient (FIPCG). Experimental results show that the proposed method can effectively improve the visualization quality of surveillance videos under heavy fog and snowing weather. Comparing with the state-of-the-art image defogging methods, the proposed method is much more efficient.
KeywordsVideo Frame Surveillance Video IEEE Conf Foreground Object Foreground Region
Unable to display preview. Download preview PDF.
- 1.Narasimhan, S., Nayar, S.: Chromatic framework for vision in bad weather. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR, Hilton Head, SC, USA, pp. 598–605 (2000)Google Scholar
- 2.Nayar, S., Narasimhan, S.: Vision in bad weather. In: Proc. International Conference on Computer Vision, ICCV, Kerkyra, Corfu, Greece, pp. 820–827 (1999)Google Scholar
- 3.Shwartz, S., Namer, E., Schechner, Y.: Blind haze separation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR, New York, NY, USA, pp. 1984–1991 (2006)Google Scholar
- 4.Tan, R.: Visibility in bad weather from a single image. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR, Anchorage, Alaska, USA (2008)Google Scholar
- 5.Fattal, R.: Single image dehazing. In: Proc. SIGGRAPH, Los Angeles, California, USA (2008)Google Scholar
- 6.He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR, Miami, Florida, USA, pp. 1956–1963 (2009)Google Scholar
- 7.Ancuti, C., Ancuti, C., Bekaert, P.: Effective single image dehazing by fusion. In: Proc. IEEE Conf. International Conference on Image Processing, ICIP, Hong Kong, China (2010)Google Scholar
- 10.Dong, W., Jia, Z., Shao, J., Li, Z., Liu, F., Zhao, J., Peng, P.Y.: Adaptive object detection and visibility improvement in foggy image. Journal of Multimedia 6 (2011)Google Scholar
- 11.Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR, New York, NY, USA, pp. 61–68 (2006)Google Scholar
- 13.Yuk, J.S.C., Wong, K.Y.K.: An efficient pattern-less background modeling based on scale invariant local states. In: Proc. IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS, Klagenfurt, Austria, pp. 285–290 (2011)Google Scholar