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Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle

机译：在移动车辆的交通标志检测中利用时间和空间限制

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摘要

This paper addresses detection, tracking and recognition of traffic signs in video. Previous research has shown that very good detection recalls can be obtained by state-of-the-art detection algorithms. Unfortunately, satisfactory precision and localization accuracy are more difficultly achieved. We follow the intuitive notion that it should be easier to accurately detect an object from an image sequence than from a single image. We propose a novel two-stage technique which achieves improved detection results by applying temporal and spatial constraints to the occurrences of traffic signs in video. The first stage produces well-aligned temporally consistent detection tracks by managing many competing track hypotheses at once. The second stage improves the precision by filtering the detection tracks by a learned discriminative model. The two stages have been evaluated in extensive experiments performed on videos acquired from a moving vehicle. The obtained experimental results clearly confirm the advantages of the proposed technique.
机译：本文致力于视频中交通标志的检测，跟踪和识别。先前的研究表明，可以通过最新的检测算法获得很好的检测召回率。不幸的是，更难以获得令人满意的精度和定位精度。我们遵循直觉的观念，即从图像序列中准确检测对象比从单个图像中检测对象容易。我们提出了一种新颖的两阶段技术，该技术通过将时间和空间约束应用于视频中的交通标志的出现来实现改进的检测结果。第一阶段通过一次管理许多相互竞争的轨道假设，产生时间对准的，时间一致性良好的检测轨道。第二阶段通过学习的判别模型对检测轨迹进行滤波，从而提高了精度。这两个阶段已在对从行驶中的车辆获取的视频进行的广泛实验中进行了评估。获得的实验结果清楚地证实了所提出技术的优点。

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