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Image based roadside POI recognition and GPS correction scheme using smart phones

机译:使用智能手机的基于图像的路边POI识别和GPS校正方案

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In recent years, advanced driver assistance system (ADAS) and intelligent traffic system (ITS) both are popular issues. This paper presents an efficient and accurate roadside sign recognition which can be used to collect traffic sign and point of interest (POI) information by any end-users with smart phones or driving video recorders. In order to achieve high recognition performance for the proposed scheme, the local binary pattern (LBP) operator is used for feature extraction with low computing complexity scheme, and the support vector machine (SVM) is also used to learn and classify roadside signs. Besides, this paper also utilizes an image based distance measurement method to obtain the distance between roadside signs and cameras by single camera view, thus the proposed scheme can be applied to general electronic devices with single camera such as smart phones, tablets, driving video recorders. The POI and distance information can be applied in ADAS, intelligent traffic system, navigation system, and GPS correction. Experimental results show the average accuracy of recognition for each roadside sign is 98.1873%, and the average recognition speed is less than 20 milliseconds per each sign. Also, the distance measurement method is workable to correct the GPS positions of POIs or users.
机译:近年来,高级驾驶员辅助系统(ADAS)和智能交通系统(ITS)都是很受欢迎的问题。本文提出了一种有效且准确的路边识别技术,任何具有智能手机或行车记录仪的最终用户都可以使用该技术来收集交通标志和兴趣点(POI)信息。为了实现所提出方案的高识别性能,使用本地二进制模式(LBP)运算符以低计算复杂度的方案进行特征提取,并使用支持向量机(SVM)对路标进行学习和分类。此外,本文还利用基于图像的距离测量方法,通过单摄像机视图获得路边标志与摄像机之间的距离,因此该方案可以应用于具有单摄像机的一般电子设备,例如智能手机,平板电脑,行车记录仪。 。 POI和距离信息可以应用在ADAS,智能交通系统,导航系统和GPS校正中。实验结果表明,每个路标的平均识别准确率为98.1873%,每个路标的平均识别速度小于20毫秒。而且,距离测量方法可用于校正POI或用户的GPS位置。

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