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CVPR
1998
IEEE
14 years 10 months ago
Joint Probabilistic Techniques for Tracking Multi-Part Objects
Common objects such as people and cars comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying ch...
Christopher Rasmussen, Gregory D. Hager
ICIP
2005
IEEE
14 years 10 months ago
Joint feature-spatial-measure space: a new approach to highly efficient probabilistic object tracking
In this paper we present a probabilistic framework for tracking objects based on local dynamic segmentation. We view the segn to be a Markov labeling process and abstract it as a ...
Feng Chen, XiaoTong Yuan, ShuTang Yang
CVPR
2010
IEEE
13 years 8 months ago
A probabilistic framework for joint segmentation and tracking
Most tracking algorithms implicitly apply a coarse segmentation of each target object using a simple mask such as a rectangle or an ellipse. Although convenient, such coarse segme...
Chad Aeschliman, Johnny Park, Avinash C. Kak
ISBI
2008
IEEE
14 years 9 months ago
A new detection scheme for multiple object tracking in fluorescence microscopy by joint probabilistic data association filtering
Tracking of multiple objects in biological image data is a challenging problem due largely to poor imaging conditions and complicated motion scenarios. Existing tracking algorithm...
Ihor Smal, Wiro J. Niessen, Erik H. W. Meijering
CVPR
2003
IEEE
14 years 10 months ago
Probabilistic Tracking in Joint Feature-Spatial Spaces
In this paper we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object a...
Ahmed M. Elgammal, Ramani Duraiswami, Larry S. Dav...