Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Feature Selection for Background Subtraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Interactive feedback for video tracking using a hybrid maximum likelihood similarity measure
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
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Foreground segmentation is an elementary process in an intelligent visual surveillance system.When a background is changed dynamically, it is difficult to distinguish the background from the foreground. In this paper, we propose an unified statistical framework of foreground segmentation and motion estimation method. In this method, a motion prior distribution is recursively estimated by a particle filter model. The motion prior probability plays a key role as a weight of each pixelwise observation model in classifying a pixel as background or foreground. We use a kernel density estimation method for the observation model. Using a temporal diffusion kernel, we emphasize recent observations. A soft selective updating rule is also suggested and this rule can overcome a deadlock problem.The algorithm can be applied to images acquired from a fixed camera. Experimental results with many real image sequences showed the validity of our method.