Adaptive background estimation of underwater using Kalman-Filtering

Fast and accurate estimation of background model in video sequences is a basic task in many computer vision and video analysis applications. Underwater vision is a new area and the background of underwater has special quality such as unstable light spot, water ripple. To this end, this paper proposes an algorithm based on Kalman Filter, which is applied to the estimation of dynamic underwater background with a static monitoring camera of swimming pool's bottom. Experimental on several underwater video sequences performing the model can efficiently adapt to the environmental of underwater.

[1]  Stan Sclaroff,et al.  Segmenting foreground objects from a dynamic textured background via a robust Kalman filter , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Olaf Munkelt,et al.  Adaptive Background Estimation and Foreground Detection using Kalman-Filtering , 1995 .

[3]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  S. Kamel,et al.  Moving object removal in video sequence and background restoration using kalman filter , 2008, 2008 International Symposium on Telecommunications.

[5]  Zhang Shu-jiang,et al.  Digital Image Stabilization Using Kalman Filtering , 2007 .

[6]  M. Balcilar,et al.  Extracting vehicle density from background estimation using Kalman filter , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[7]  Liu Ya.  MOVING OBJECT DETECTION AND TRACKING BASED ON BACKGROUND SUBTRACTION , 2002 .