Lighting invariant urban street classification
Lighting invariant urban street classification
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DOI：
10.1109/icra.2014.6907082
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
B. Upcroft;C. McManus;W. Churchill;William P. Maddern;P. Newman
中科院分区：
文献类型：
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作者：
B. Upcroft;C. McManus;W. Churchill;William P. Maddern;P. Newman
在本文中，我们提出了混合使用光源不变和RGB图像进行城市场景的图像分类，尽管具有挑战性的变化，照明条件。应对照明变化（以及由此引起的阴影）是使用视觉长期自主的不可谈判的要求。这方面的一个方面是能够可靠地分类场景组件在存在显着的，往往是突然变化的照明。这是本文的重点。提出的任务是从一个完整的彩色图像中的场景中的所有部分进行分类，我们建议，照明不变变换可以减少场景的变化，从而在一个更可靠的分类。我们利用“数据传输”的思想进行分类，从全色图像开始，使用全局图像描述符获得候选场景级匹配。这之后通常是与局部特征的超像素级匹配。然而，我们表明，如果在计算超像素级特征之前对RGB图像进行光源不变变换，则分类对场景照明效果的鲁棒性会显着提高。该方法使用三个数据集进行评估。第一个是我们自己的数据集，第二个是KITTI数据集，使用手动生成的地面实况进行定量分析。我们定性评估的方法在第三个自定义数据集超过750米的轨迹。
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.