The extensive proliferation of smartphones presents a new opportunity for precise indoor and outdoor location-based services. Although various smartphone-based positioning technologies have been investigated, no practical and economical method has been developed where global positioning system (GPS) cannot be applied. Since it is difficult to estimate travel distance accurately with a low-cost inertial sensor embedded in a smartphone, several methods have been proposed to estimate stride. However, the estimation of stride is not accurate as it depends on walking posture. In this article, a novel approach is proposed to estimate the travel distance of the smartphone using optical flow (OF) technology, which is applied to the video image captured from the smartphone camera. Several algorithms have been developed to solve various problems that occur when a user walks holding a smartphone. The feature trace method proposed in this article improves the travel distance estimation accuracy greatly. Several motion compensation methods for the OF are proposed also using smartphone sensor data. The performance of the proposed methods is verified by experiments in three different places with two kinds of smartphones. The estimation accuracy of the travel distance by the proposed method is obtained from all the experimental data. As a result, the average travel distance error rate is 2.6%; for the 5% distance error rate, the error CDF is 86%; and for the 10%, it is 100%. The proposed methods could be applied to track the smartphone of the pedestrian with precise position accuracy indoors and outdoors.

中文翻译：

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使用光流和智能手机传感器估计行人行驶距离


智能手机的广泛普及为精确的室内和室外定位服务提供了新的机遇。尽管已经研究了各种基于智能手机的定位技术，但尚未开发出无法应用全球定位系统（GPS）的实用且经济的方法。由于智能手机中嵌入的低成本惯性传感器很难准确估计行进距离，因此提出了几种估计步幅的方法。然而，步幅的估计并不准确，因为它取决于步行姿势。在本文中，提出了一种利用光流（OF）技术来估计智能手机行进距离的新颖方法，该技术应用于从智能手机摄像头捕获的视频图像。人们已经开发了多种算法来解决用户拿着智能手机行走时出现的各种问题。本文提出的特征追踪方法大大提高了行驶距离估计的精度。还使用智能手机传感器数据提出了几种 OF 运动补偿方法。通过在三个不同地方使用两种智能手机进行的实验验证了所提出方法的性能。从所有实验数据中获得了所提出方法的行驶距离估计精度。结果，平均行驶距离错误率为2.6%；对于5%的距离误差率，误差CDF为86%；对于10%来说，就是100%。所提出的方法可用于跟踪行人的智能手机，在室内和室外具有精确的位置精度。