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Learning enhanced 3d models for vehicle tracking (1998)
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| Venue: | In British Machine Vision Conference |
| Citations: | 22 - 5 self |
BibTeX
@INPROCEEDINGS{Ferryman98learningenhanced,
author = {J. M. Ferryman and A. D. Worrall and S. J. Maybank},
title = {Learning enhanced 3d models for vehicle tracking},
booktitle = {In British Machine Vision Conference},
year = {1998},
pages = {873--882}
}
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Abstract
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. 1
Keyphrases
vehicle tracking object recognition iconic matching enhanced hypothesis verification strategy unified approach model-based tracking improved hypothesis verification computer vision feature representation eigenspace representation real-world image sequence new learning methodology appearance-based representation iconic evaluation technique traffic surveillance domain
