Object tracking typically relies on a dynamic model to
predict the object’s location from its past trajectory. In
crowded scenarios a strong dynamic model is particularly
impo...
Abstract. Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model i...
Vikas Reddy, Conrad Sanderson, Andres Sanin, Brian...
The precise alignment of a 3D model to 2D sensor images to recover the pose of an object in a scene is an important topic in computer vision. In this work, we outline a registrati...
We boost the efficiency and robustness of distributionbased matching by random subsampling which results in the minimum number of samples required to achieve a specified probabili...
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...