# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 2 Related Work
### Vision-Based Indoor Localization  
Vision-based indoor positioning requires the user to actively participate in positioning using a camera, and is characterized by using a visual landmark to derive stable results compared to other methods such as Wi-Fi-based positioning. However, landmarks can be recognized differently even in the same place due to variables such as lighting, block of view, and landscape changes caused by passersby. This may cause the degradation of positioning accuracy. In the case of photos other than markers, the amount of computation and memory usage increases as the image processing and storage capacity is enormous. In [26], characteristic parts of the interior space, such as store logos and murals, are registered as reference objects. Consequently, when the user photographs three objects close to their location, positioning is performed by matching them to the registered reference objects. After object matching, the user's position is measured by triangulation using the position of each object and the angle information of the gyroscope. This method does not require additional infrastructure, and the maintenance costs can be lowered as it is based on an object that has little movement compared to methods in which the signal environment can change frequently. However, if there are many unregistered reference objects, the positioning process and continuous positioning becomes impossible. [PERSON] et al. [27] proposed a system called ViNav that could be used without installing indoor maps, radio maps, or additional hardware. It collected photos or videos through crowdsourcing and 3-D modeling of the interior structure using structure-from-motion technology. At the time, the location labeling of the photo or video was performed by estimating the movement path of the user with the IMU, and geo-referencing the Wi-Fi fingerprint according to the movement path. The barometer estimated the location of the connection among the floors, such as stairs or elevators. This system had the advantage of being able to build maps through crowdsourcing and to be continuously updated so that no additional infrastructure was required. However, active participation of the user was required and the image data were large, so the required memory and computational complexity increased. Methods that utilize vision, such as [26; 27], often process queries on the server because the required memory and computational requirements of the image data are large. This makes it difficult to scale up the system and reduce the positioning response time. In addition, network connections must be made continuously, and there could be controversy over issues of privacy. To overcome this problem, [PERSON] et al. [28] proposed a system called HAIL that automates the image verification step. HAIL is an offline feature selection that reduces memory and computational complexity as it only stores and utilizes characteristic features for landmarks. Because the capacity is not large, positioning is performed on the user's smartphone rather than on the server, so it can be used without a network connection. In addition, it reduces matching errors by using only characteristic features. Although it has the advantage of overcoming capacity and computational problems, which are the characteristic disadvantages of vision-based methods, it could be difficult to use effectively in places where characteristic features are weak. There are many indoor areas with less texture, such as walls, ceilings, floors, and windows. InLoc [29] uses dense feature extraction based on a convolutional neural network (CNN) instead of sparse feature extraction in order to find and utilize meaningful features in this environment. InLoc has the advantage of reducing positioning confusion in complex indoor structures by considering negative evidence as well as positive evidence during feature matching. However, RGB-D data are indispensable and have limitations in that users must actively intervene while taking pictures.