# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 2 Related Work
### Marker-Based Indoor Localization  
Indoor positioning technologies using QR codes or markers primarily show positioning results based on AR and develop and use various markers by themselves. [PERSON] et al. [16] used the Vuforia system to provide marker-based indoor navigation services in libraries. In this study, after registering photos of major locations in the library and AR objects corresponding to them in the system, the AR object guides the users when they project the registered photos. In the Vuforia system, multiple markers can be recognized simultaneously, and objects can be registered without creating additional markers. However, when there are multiple target images, only AR object expression experiments and surveys are conducted, and additional experiments are needed for the availability of actual navigation services. [PERSON] et al. [17] proposed an indoor navigation framework using ARToolKit markers developed in previous research. The provided application was Android-based, with two operating modes: an administrator mode that registered a marker on an indoor map after attaching a marker to the ceiling, and a user mode that searched for directions while recognizing the marker by means of a mobile device. This system is easy to install, use, manage, and expand Android applications, and provides convenience through voice and text guidance. However, the direction of the marker must always be parallel to the direction of the corridor, and the mobile device must always face the ceiling as the marker itself is ceiling-installed. Therefore, this system may experience difficulties in installing markers and place an undesirable burden on users. Navigation services for people with visual impairments have also been studied [18; 19]. In [18], adaptive artificial potential field (AAPF) path planning was proposed to enable more efficient and safer path planning. In addition, haptic gloves were used to communicate between the user and the smartphone to provide stable guidance--while the left glove guided the road, and the right glove informed obstacles. However, in addition to the inconvenience of wearing a haptic device, there were limitations in that the system required a digital map. In [19], IP cameras were installed on the ceilings of major places, and remote systems were built. The installed IP cameras recognized the marker attached to a user's head, estimated the location, and guided the user. It was possible to set the destination by voice, increasing the convenience for the visually impaired. In this system, there was no need to search for markers because the directions were guided by voice and sound, the system recognizing various obstacles and guiding the user through the modified route. However, it was costly to build the required infrastructure (such as an IP camera). Engfi Gate [20] is a building guidance system created by combining three systems. The marker-based cyber-physics interaction system receives input from the user through visible and invisible markers and operates the device according to the input. The indoor positioning system estimates the user's location using markers, and the AR system provides information necessary for guidance to the user's mobile phone or head-mounted display (HMD). The reliability was improved by using two types of markers, and convenience was improved by providing an application that supported 3-D and HMD. However, there were no experimental results for the entire system, and the accuracy of positioning using the received signal strength indicator value of the Bluetooth low energy module was lowered. [PERSON] et al. [21] processed the augmentation and placement of the indoor environment into virtual indoor model data in an AR device (HoloLens) using marker-based localization. After determining the pose of the AR device for the coordinate system of the real physical world, building information modeling data were overlaid. [PERSON] et al. [22] proposed an indoor positioning system based on image processing using OpenCV and Python.