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Multi-view traffic sign detection, recognition, and 3D localisation

机译：多视图交通标志检测，识别和3D本地化

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Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. We used a van with eight roof-mounted cameras to drive through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs from such images. The task is challenging, as illumination conditions change regularly, occlusions are frequent, sign positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis. For the initial detection in single frames, we use a set of colour- and shape-based criteria. They yield a set of candidate sign patterns. The selection of such candidates allows for a significant speed up over a sliding window approach while keeping similar performance. A speedup is also achieved through a proposed efficient bounded evaluation of AdaBoost detectors. The 2D detections in multiple views are subsequently combined to generate 3D hypotheses. A Minimum Description Length formulation yields the set of 3D traffic signs that best explains the 2D detections. The paper comes with a publicly available database, with more than 13,000 traffic signs annotations.
机译：一些应用程序需要有关街道家具的信息。任务的一部分是调查所有交通标志。这必须在数百万公里的道路上完成，并且需要经常重复进行此练习。我们用一辆装有八个屋顶摄像机的货车在街道上行驶，每米拍摄一次图像。本文提出了一种从这些图像中有效检测和识别交通标志的管道。这项任务具有挑战性，因为照明条件会定期变化，遮挡频繁，标志的位置和方向有很大不同，并且在相同类型中实际标志的相似性远不如人们预期的那样。我们将2D和3D技术相结合，以改善结果，使之超越现有技术（单视图分析仍然非常关注）。对于单个帧中的初始检测，我们使用一组基于颜色和形状的标准。它们产生一组候选符号模式。选择此类候选对象可以在保持相似性能的同时，大大提高滑动窗口方法的速度。通过提议的AdaBoost检测器的有效有界评估，也可以实现加速。随后将多个视图中的2D检测合并以生成3D假设。 “最小描述长度”公式可产生最能解释2D检测的3D交通标志集。该文件带有一个公共数据库，其中包含13,000多个交通标志注释。

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