无约束车牌和文本定位和识别

Jiri Matas, K. Zimmermann
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引用次数: 119

摘要

车牌和交通标志的检测和识别有许多与交通系统相关的不同应用,例如交通监测、检测被盗车辆、驾驶员导航支持或任何统计研究。已经提出了许多方法,但仅适用于特定情况和在约束下工作(例如已知的文本方向或高分辨率)。因此,提出了一种新的基于极值区域的局部阈值可分离检测器,它可以通过机器学习技术适应任意形状。在不同视点(-45/spl度/,45/spl度/),尺度(从7到数百像素高度)的车牌图像测试集中,即使在较差的光照条件和部分遮挡下,也能达到较高的检测精度(95%)。最后通过对交通标志的检测,给出了检测器的通用能力。检测器内的标准分类器(神经网络)选择极端区域的相关子集,即阈值图像的连接组件的区域。极端区域的特性使检测器对光照变化和部分遮挡具有很强的鲁棒性。对视点变化的鲁棒性是通过使用不变描述符和/或通过对分类器的形状变化进行建模来实现的。检测的时间复杂度在像素数量上近似为线性,而在高端PC上,对于640 /spl times/ 480的图像,非优化实现的运行速度约为每秒1帧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unconstrained licence plate and text localization and recognition
Licence plates and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Therefore a new class of locally threshold separable detectors based on extremal regions, which can be adapted by machine learning techniques to arbitrary shapes, is proposed. In the test set of licence plate images taken from different viewpoints (-45/spl deg/,45/spl deg/), scales (from seven to hundreds of pixels height) even in bad illumination conditions and partial occlusions, the high detection accuracy is achieved (95%). Finally we present the detector generic abilities by traffic signs detection. The standard classifier (neural network) within the detector selects a relevant subset of extremal regions, i.e. regions that are connected components of a thresholded image. Properties of extremal regions render the detector very robust to illumination change and partial occlusions. Robustness to a viewpoint change is achieved by using invariant descriptors and/or by modelling shape variations by the classifier. The time-complexity of the detection is approximately linear in the number of pixel and a non-optimized implementation runs at about 1 frame per second for a 640 /spl times/ 480 image on a high-end PC.
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