Lighting invariant urban street classification

Upcroft, Ben, McManus, Colin, Churchill, Winston, Maddern, Will, & Newman, Paul (2014) Lighting invariant urban street classification. In Proceedings of the 2014 IEEE International Conference on Robotics & Automation (ICRA), IEEE, Hong Kong Convention and Exhibition Center, Hong Kong, China, pp. 1712-1718.

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In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence of marked and often sudden changes in lighting. This is the focus of this paper. Posed with the task of classifying all parts in a scene from a full colour image, we propose that lighting invariant transforms can reduce the variability of the scene, resulting in a more reliable classification. We leverage the ideas of “data transfer” for classification, beginning with full colour images for obtaining candidate scene-level matches using global image descriptors. This is commonly followed by superpixellevel matching with local features. However, we show that if the RGB images are subjected to an illuminant invariant transform before computing the superpixel-level features, classification is significantly more robust to scene illumination effects. The approach is evaluated using three datasets. The first being our own dataset and the second being the KITTI dataset using manually generated ground truth for quantitative analysis. We qualitatively evaluate the method on a third custom dataset over a 750m trajectory.

Impact and interest:

2 citations in Scopus
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ID Code: 78776
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1109/ICRA.2014.6907082
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Deposited On: 19 Nov 2014 05:57
Last Modified: 21 Nov 2014 11:33

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