The Trace Model for Object Detection and Tracking

12 years 3 months ago
The Trace Model for Object Detection and Tracking
We introduce a stochastic model to characterize the online computational process of an object recognition system based on a hierarchy of classifiers. The model is a graphical network for the conditional distribution, under both object and background hypotheses, of the classifiers which are executed during a coarse-to-fine search. A likelihood is then assigned to each history or "trace" of processing. In this way, likelihood ratios provide a measure of confidence for each candidate detection, which markedly improves the selectivity of hierarchical search, as illustrated by pruning many false positives in a face detection experiment. This also leads to a united framework for object detection and tracking. Experiments in tracking faces in image sequences demonstrate invariance to large face movements, partial occlusions, changes in illumination and varying numbers of faces.
Sachin Gangaputra, Donald Geman
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CLOR
Authors Sachin Gangaputra, Donald Geman
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