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BMVC
1998

Learning Enhanced 3D Models for Vehicle Tracking

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Learning Enhanced 3D Models for Vehicle Tracking
This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.
James M. Ferryman, Anthony D. Worrall, Stephen J.
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where BMVC
Authors James M. Ferryman, Anthony D. Worrall, Stephen J. Maybank
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