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Vehicle matching and recognition under large variations of pose and illumination

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3 Author(s)
Tingbo Hou ; Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY, USA ; Sen Wang ; Hong Qin

Matching vehicles subject to both large pose transformations and extreme illumination variations remains a technically challenging problem in computer vision. In this paper, we develop a new and robust framework toward matching and recognizing vehicles with both highly varying poses and drastically changing illumination conditions. By effectively estimating both pose and illumination conditions, we can re-render vehicles in the reference image to generate the relit image with the same pose and illumination conditions as the target image. We compare the relit image and the re-rendered target image to match vehicles in the original reference image and target image. Furthermore, no training is needed in our framework and re-rendered vehicle images in any other viewpoints and illumination conditions can be obtained from just one single input image. Experimental results demonstrate the robustness and efficacy of our framework, with a potential to generalize our current method from vehicles to handle other types of objects.

Published in:

Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on

Date of Conference:

20-25 June 2009