# General Moving Object Localization from a Single Flying Camera
## 8 Conclusions  
This paper proposed a novel system to estimate the location of an object from a single moving camera mounted on a drone. The proposed algorithm aimed to fulfil two objectives: (1) to be able to make the baseline between cameras arbitrarily large, and (2) to be able to remove the depth ambiguity caused by a moving object relative to a moving camera. We have shown in our previous work [16] that, by using three images, we are able to recover the location of an object moving in a direction parallel to the motion of the camera. In this research, we showed that, by using four images, we are able to recover the location of an object moving linearly in an arbitrary direction.  
Figure 22: False detection of pedestrians.  
The algorithm works by instructing the drone to fly in a specific pattern with acceleration, which allows us to use a varying set of camera baselines to compute the object position, thus avoiding the depth ambiguity. To maintain generality, we do not assume that we know the class of the object (e.g., person, vehicle, farming equipment, etc.) or the size, speed, and direction of motion of the object. This allows us to apply this technique to various applications, such as locating farm personnel and equipment, mining vehicles, delivery vehicles belonging to logistics supply chains, wild animals in a prairie, etc. Our evaluation results show that our algorithm can achieve a depth-error rate of \(<\)8%, and it is equal or better in terms of localization accuracy when compared to other state-of-the-art algorithms.  
The proposed algorithm will be very useful for drones with a single camera, including lightweight or low-cost drones. The algorithm can be used in a vast number of applications, such as drone navigation, intelligent CCTV systems, pursuit missions, military missions, etc. For example, this system can be used as a mobile intelligent CCTV system for both indoors and outdoors, and has the ability to locate suspicious entities (such as in supermarkets, factories, private estates, government properties, etc.). Additionally, the algorithm can help in situations when a drone pursues a distant target, such as following a suspected criminal (and avoiding detection by the suspect and avoiding hostile actions on the drone), following endangered animal species (with stealth, avoiding close encounter), and performing military reconnaissance missions (since the system is \"passive\", it does not emit any signals).  
Conceptualization, K.-C.Y.; methodology, K.-C.Y. and I.K.; software, I.K.; validation, I.K.; formal analysis, K.-C.Y. and I.K.; investigation, K.-C.Y. and I.K.; resources, K.-C.Y.; data curation, I.K.; writing--original draft preparation, I.K.; writing--review and editing, K.-C.Y.; visualization, I.K.; supervision, K.-C.Y.; project administration, K.-C.Y.; funding acquisition, K.-C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1A02019235).  
The authors declare no conflict of interest.