# General Moving Object Localization from a Single Flying Camera
## 2 Related Works
[27] described a system that uses stereo vision and laser odometry for drone navigation; however, carrying two cameras at once consumes more power and decreases the payload as well as the duration of flight. For lightweight or low-cost drones, stereo cameras cannot be a solution since the drones cannot afford to even carry the additional camera. [PERSON] et al. [28] proposed a navigation system for low-cost micro aerial vehicles (MAVs), using two Kalman filters in the extended and error-state flavors. Their system used a single-camera system and is based on low-cost sensors. This is the reason why all the drones on the market currently come with only a single camera. Another solution is to use pre-determined information to localize objects with a single camera. If we know the information about the target object, such as its size, shape, and color, we can immediately calculate the distance to the object using simple ratio calculations. [PERSON] and [PERSON] [29] showed one good example of such a system. They performed efficient calibration for multiple PTZ cameras by using a single object lying on a horizontal plane with known parameters. Their algorithm works by detecting A4 paper, estimating the angle and altitude of one camera, projecting the A4 paper's location on a 3D space, and then calibrating other cameras with this information. For drones, this method of using prior information is very simple and has no effects on the duration of flight and payload, since it needs no additional equipment. [PERSON] and [PERSON] [30] proposed a system where a drone estimates relative altitude from a ground object with (or without) movement if the size of the object is known. [PERSON] et al. [31] performed a depth estimation of color markers for the automatic landing control of Unmanned Aerial Vehicles (UAVs) using stereo vision with a single camera. In their work, the color markers are static (i.e., not moving) and are close to the drone (below 3 m). [PERSON] et al. [32] also extended the concept of automatic landing control for a fixed-wing UAV. Like their earlier work, they took images with a single downward looking camera at different time intervals, and they used static color markers on the flat ground. [PERSON] et al. [33] also proposed a novel system to safely land a drone in the absence of GPS signals using a unique marker designed as a tracking target during landing procedures. Meanwhile, object localization can be performed with a single camera with image analysis. This approach uses feature extraction from images to perform navigation and depth extraction. [PERSON] et al. [34] proposed an accurate autonomous indoor navigation system for a quad copter with a monocular vision. They used information from multiple sensors from the drone as well as a vision-based Simultaneous Localization and Mapping (SLAM) system and Extended Kalman Filter (EKF) to achieve robust and accurate 3D position and velocity estimation. However, they require a large amount of time (about 1 min) with respect to their traveling distance (around 8 m), and their research is confined to an indoor environment which covers a small area. The solution we are looking for is to use stereo vision principles but with a single-camera system instead. This can be achieved by taking multiple images of the object with a camera that is moving in a certain pattern so as to get the required baseline and image disparity. This system is also free from the decrease in payload and duration of flight, since it does not require any additional hardware gadgets. Thus far, we have found few related works that address these requirements. [PERSON] et al. (2018) did propose a novel monocular vision-based environmental perception approach and implemented it in an unmanned aerial vehicle system for close-proximity transmission tower inspection. Their proposed framework comprises tower localization and an improved point-line-based simultaneous localization and mapping framework consisting of feature matching, frame tracking, local mapping, loop closure, and nonlinear optimization.