Improving Object Classification in Far-Field Video

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Improving Object Classification in Far-Field Video
Object classification in far-field video sequences is a challenging problem because of low resolution imagery and projective image distortion. Most existing far-field classification systems are trained to work well in a constrained set of scenes, but can fail dramatically when applied to new scenes, or even different views of the same scene. We identify discriminative object features for classifying vehicles and pedestrians and develop a scene-invariant classification system that is trained on a small number of labelled examples from a few scenes, but transfers well to a wide range of new scenes. Simultaneously, we demonstrate that use of scene-specific context features (such as image position and direction of motion of objects) can greatly improve classification in any given scene. To combine these ideas, we propose a new algorithm for adapting a scene-invariant classifier to scene-specific features by retraining with the help of unlabelled data in a novel scene. Experimental results...
Biswajit Bose, W. Eric L. Grimson
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Biswajit Bose, W. Eric L. Grimson
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