We propose a novel statistical method for motion detection and background maintenance for a mobile observer. Our method is based on global motion estimation and statistical backgro...
We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, w...
Leonid Sigal, Ying Zhu, Dorin Comaniciu, Michael J...
Autonomous robots use sensors to perceive and track objects in the world. Tracking algorithms use object motion models to estimate the position of a moving object. Tracking effic...
Abstract— Robots need to track object. Object tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. Interestingly, when mult...
We propose a novel nonlinear, probabilistic and variational method for adding shape information to level setbased segmentation and tracking. Unlike previous work, we represent sha...