Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
MODEEP: a motion-based object detection and pose estimation method for airborne FLIR sequences
Machine Vision and Applications
Multiview Constraints on Homographies
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Rank 4 Constraint in Multiple (=3) View Geometry
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Empirical Bayesian EM-based Motion Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mosaics of Scenes with Moving Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Statistical background modeling is a standard technique for the detection of moving objects in a static scene. Nevertheless, the state-of-the-art approaches have several lacks for short sequences or quasi-stationary scenes. Quasi-static means that the ego-motion of the sensor is compensated by image processing. Our focus of attention goes back to the modeling of the pixel process, as it was introduced by Stauffer and Grimson. For quasi-stationary scenes the assignment of a pixel to an origin is uncertain. This assignment is an independent random process that contributes to the gray value. Since the typical update schemes are biased we introduce a novel update scheme based on the join mean and join variance of two independent distributions. The presented method can be seen as an update for the initial guess for more sophisticated algorithms that optimize the spatial distribution.