# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 2 Related Work
### Vision-Based Indoor Localization
See-Your-Room [30] is a 3-D localization system that utilizes public cameras such as CCTV. In the detection phase, the position and pose of the people are captured. Mask Recurrent-CNN (R-CNN) distinguishes between human pixels and non-human pixels. In the mapping step, projection transformation using a perspective matrix is used to convert the position of a person into a position on the indoor map. Because it uses a public camera, there is no need for the participation of the positioning targets, but since it does not distinguish individual people, only the system knows that a person is in a certain location. Therefore, because the positioning targets are not assumed to be users and positioning is performed througha public camera, privacy issues may arise. Much the same way that selecting partitions for image matching using a Wi-Fi fingerprint reduces complexity in [27], the movement distance and direction are estimated using an IMU sensor and Wi-Fi channel state information (CSI) for feature matching in [31]. When a user takes two pictures of the same place at slightly different locations, spatial information is derived by obtaining data on the location and direction in which the two pictures were taken through the IMU and CSI. Using this method, images are compared based on the point of interest (POI), not at a pixel-level. There are studies that have tried to improve accuracy by combining vision and PDR techniques [13; 32]. [PERSON] et al. [32] compared two algorithms, ORB-SLAM and UcoSLAM, for vision-based positioning through a camera. Robustness was improved by mixing vision and PDR techniques, and the possibility of confusion in positioning was low when using a marker. iPAC [13] uses images obtained through public cameras, as shown in [5], and combines PDR and vision with a motion-sequence-based matching algorithm. While not identifying individuals in [30], this study tracks people by assigning a unique ID to each person in a real-time video. Positioning services can be provided without user participation, and if a surveillance camera is already available, a separate infrastructure installation is not required. Although computing time is reduced by using an algorithm with low computational complexity, privacy issues can arise as the server side can identify individuals along with their appearance, location, and movement. Travi-Navi [33] is a navigation service built with user participation. This system records landmarks such as photographs obtained by taking images of the paths that the guider passes, sensor values during walking, and radio signals and uses them for positioning. To acquire a clear photographic image during walking, an accelerometer is used to automatically take a picture at stable moments. The robustness is improved by using various types of data together, and the user can build a navigation service on their own without a floor plan or pre-deployed localization system. Therefore, the proposed system can be used to build an effective positioning system using crowdsourcing. However, because a user needs to carry the smartphone in a state in which it is possible to take pictures continuously, the proposed method may cause inconvenience for them. In addition, the amount of computational complexity and energy consumption increases due to frequent data processing.