# General Moving Object Localization from a Single Flying Camera
## 2 Related Works  
A very popular solution for object localization (and object tracking) is to use drones. The most direct way to achieve this is to get the drone to fly directly to the object and then retrieve its global positioning system (GPS) location. Tracking can be achieved simply by following the object when it moves. However, this very simple approach is inefficient, and it is also not possible to localize multiple objects simultaneously. A better approach is to use stereo vision on drones. [PERSON] et al. [17] proposed an onboard stereo system for a drone to perform pursuit as well as sense and avoid. [PERSON] and [PERSON] [18] used a drone-based stereo camera system to assist a low-vision person with environment awareness by detecting a user's heading and direction. [PERSON] and [PERSON] [19] showed the possibility of using stereo vision with the Inertial Measurement Unit (IMU) to form a tightly coupled Visual-Inertial Navigation System (VINS), which shows a better performance in terms of robustness and accuracy. There is no restriction on the number of cameras for the stereo vision method. [PERSON] and [PERSON] [20] performed real-time depth mapping using a low-cost camera array of 16 cameras. Compared to the single camera pair, depth mapping becomes more accurate by increasing the number of camera pairs. Stereo vision has also been shown to be able to recover distances accurately for near objects. [PERSON] and [PERSON] [21] introduced a novel stereo rectification method for a dual-fixed Pan-Tilt-Zoom (PTZ) camera system that can recover distance accurately for up to 20 m indoors. [PERSON] et al. [22] proposed a stereo camera system that can perform face detection, with good results for the detection rate (99.92%) in an indoor environment (5 to 20 m). [PERSON] et al. [23] presented a novel camera calibration method based on the least square method that can improve the accuracy of stereo vision systems for three-dimensional point localization up to a distance of 20 cm. Detecting and localizing moving objects in a stereo vision system is also not a problem. [PERSON] et al. [24] proposed a stereo camera rig with an ultra-wide baseline distance and conventional cameras with fish-eye lenses. They used a unique method to restore the 3D positions by developing a relationship between the pixel dimensions and distances in an image and real-world coordinates. In their research, although the target was moving, the depth extraction results were reasonably accurate. However, their work was conducted in close range (around 2 m) and the object also moved only a little (around 900 mm). This is the biggest disadvantage of the stereo vision method when localizing objects. Since the algorithm requires the value of the distance between the two cameras (i.e., baseline, which is fixed), it is not good for localizing far objects, as there is only a small disparity between the objects in the two images. We need a much larger baseline to localize far objects. As the baseline gets shorter, the error gets larger. [PERSON] et al. [25] tried to overcome this problem by using multiple drones with a single camera on each of them. They performed research on ground obstacle mapping for Unmanned Ground Vehicles (UGV) without the aid of GPS and using two drones with a single camera on each. Their baseline is changeable, since each drone can be positioned to achieve the desired distance. [PERSON] et al. [26] used three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images to improve the precision of the target's localization. The multi-unmanned aerial vehicle cooperative target localization method can use more observation information, which results in a higher rendezvous accuracy and improved performance. The major problem with putting stereo cameras on a drone is the power and the cost. [PERSON] et al.