由於近年來人們對於定位的重視，全球定位系統（Global Positioning System, GPS）已被廣泛使用於我們生活中的應用，卻礙於建築物的干擾訊號傳播導致GPS在室內定位並不準確，因此如何在室內達到高精度定位成為人們重視的研究議題，傳統的方法是以訊號強度為基礎如：藍牙、Wi-Fi、ZigBee，通過三邊測量估算裝置位置，然而，基於訊號的定位方法容易因為室內環境的多路徑干擾，導致環境中的訊號分佈變動性大，產生高定位誤差，而近年來深度學習的蓬勃發展使研究人員藉由成熟的影像辨識技術對行人進行位置估計與室內定位，卻無法得到設備資訊以識別人員身份，為此我們提出了一種基於Wi-Fi與影像的高精度人員室內定位方法。
室內定位系統分為兩階段定位，第一階段通過使用智慧型手機收集三台Wi-Fi基地台兩個頻段2.4GHz及5GHz的訊號接受強度，並以機器學習方法進行粗精度定位預測，接著在第二階段分析監視攝影機捕捉的人員畫面，並以姿態估計模型提取影像中行人們的腳點座標，再藉由直接線性轉換與線性回歸模型得到影像人員的位置，最後與第一階段的Wi-Fi定位位置進行匹配，完成可識別人員的室內定位系統。
本研究採用的實驗場域具備多遮蔽物及訊號干擾，因此我們收集2.4GHz及5GHz兩個頻段的訊號接受強度，減少2.4GHz的訊號干擾以實現更高的Wi-Fi定位精度，Wi-Fi的平均定位誤差達1.4公尺，並分析兩個頻段的定位表現。在影像定位方面我們則提出兩種用於影像中的行人腳點提取方法，並以機器學習模型減少因為鏡頭扭曲與直接線性轉換造成的誤差，結果表明我們改善後的腳點提取方法能夠降低50%的定位誤差，也指出通過機器學習模型預測的定位結果比僅以2D線性變換的誤差減少約0.4公尺，達到誤差0.4公尺的高精度室內定位。

Due to the importance of positioning in recent years, Global Positioning System (GPS) has been widely used in our life applications, whereas due to the interference of signal propagation in buildings, GPS is not accurate in indoor positioning. Therefore, how to achieve high precision positioning indoors has become an important research issue. However, the signal-based positioning method is prone to high positioning error due to the multipath interference in the indoor environment, which leads to high variability of the received signal. To this end, we propose a Wi-Fi and image-based method for high-precision indoor positioning for clients.
The first phase of the system is scheduled for using smartphones to collect the received signal strength of three Wi-Fi base stations in two frequency bands, 2.4GHz and 5GHz, and use machine learning methods to perform coarse accuracy location prediction. In the second stage we analyze the images of people captured by surveillance cameras and extract the foot position of pedestrians through pose estimation model. In order to transform the position of the feet from the camera perspective to the floor plane, we use direct linear transformation and linear regression model to obtain the more precise coordinate. The location of the person in the image is then matched with the Wi-Fi location from the smartphone to complete an indoor location system that could identify the person.
In this study, we collected the received signal strengths of 2.4GHz and 5GHz bands to reduce the signal interference, and achieved higher Wi-Fi positioning accuracy with an average distance error 1.4 meters, and analyzed the positioning performance of two bands. In terms of pedestrian estimation, we propose two methods to extract pedestrian foot position in image and use machine learning models to reduce the errors caused by lens distortion and direct linear transformation. Our result also shows that the predicted positioning result by the machine learning model is 0.4m less than the error of 2D direct linear transform only, achieving 0.4 m high accuracy positioning.

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