Computer vision in target pursuit using a UAV

Research in target pursuit using Unmanned Aerial Vehicle (UAV) has gained attention in recent years, this is primarily due to decrease in cost and increase in demand of small UAVs in many sectors. In computer vision, target pursuit is a complex problem as it involves the solving of many sub-problems which are typically concerned with the detection, tracking and following of the object of interest. At present, the majority of related existing methods are developed using computer simulation with the assumption of ideal environmental factors, while the remaining few practical methods are mainly developed to track and follow simple objects that contain monochromatic colours with very little texture variances. Current research in this topic is lacking of practical vision based approaches. Thus the aim of this research is to fill the gap by developing a real-time algorithm capable of following a person continuously given only a photo input. As this research considers the whole procedure as an autonomous system, therefore the drone is activated automatically upon receiving a photo of a person through Wi-Fi. This means that the whole system can be triggered by simply emailing a single photo from any device anywhere. This is done by first implementing image fetching to automatically connect to WIFI, download the image and decode it. Then, human detection is performed to extract the template from the upper body of the person, the intended target is acquired using both human detection and template matching. Finally, target pursuit is achieved by tracking the template continuously while sending the motion commands to the drone. In the target pursuit system, the detection is mainly accomplished using a proposed human detection method that is capable of detecting, extracting and segmenting the human body figure robustly from the background without prior training. This involves detecting face, head and shoulder separately, mainly using gradient maps. While the tracking is mainly accomplished using a proposed generic and non-learning template matching method, this involves combining intensity template matching with colour histogram model and employing a three-tier system for template management. A flight controller is also developed, it supports three types of controls: keyboard, mouse and text messages. Furthermore, the drone is programmed with three different modes: standby, sentry and search. To improve the detection and tracking of colour objects, this research has also proposed several colour related methods. One of them is a colour model for colour detection which consists of three colour components: hue, purity and brightness. Hue represents the colour angle, purity represents the colourfulness and brightness represents intensity. It can be represented in three different geometric shapes: sphere, hemisphere and cylinder, each of these shapes also contains two variations. Experimental results have shown that the target pursuit algorithm is capable of identifying and following the target person robustly given only a photo input. This can be evidenced by the live tracking and mapping of the intended targets with different clothing in both indoor and outdoor environments. Additionally, the various methods developed in this research could enhance the performance of practical vision based applications especially in detecting and tracking of objects

Eye of the swarm: real-time analysis of factors affecting dynamic UAV pursuit of a moving target

The increased use of unmanned aerial vehicles (UAVs) both of a commercial or consumer level has presented a problem of protecting nation- or company-critical sites from intelli-gence gathering, surveillance, or reconnaissance activities. Recent attacks and intrusions of restricted airspace by UAVs raise questions about how to tackle the problem of tracking autonomous malicious UAVs of increasing abilities. The use of consumer or commercial grade UAVs may provide an answer to the problem through implementing swarm formations and tactics to pursue a malicious UAV to its landing point, and thereby its operator. Swarms of UAVs can provide redundancy and group agility greater than an individual drone, as well as a larger tracking radius than fixed ground-based radars or expensive military-grade UAVs. The use of UAV swarms consisting of differing sizes and formations were examined to determine their effectiveness in pursuing a malicious UAV breaching restricted airspace. Based within a simulated environment, the modelling involved an analysis of the distance between the swarm and the malicious UAVs landing site at the end of the simulation. The effects of increasing the swarm size, the formation that the collective swarm takes, and the flight characteristics (speed, acceleration, flight path, etc.) of the malicious UAV were varied to test the relative strengths and weaknesses of the a swarm compared to the same number of UAV pursuers working independently. The results show the use of collaborative formations decrease the final distance from the target, especially in swarms containing five or more UAVs. The cone formation proved to be the overall better choice of the two collaborative formations developed and tested. This formation provided the greatest resilience in adapting to increases in malicious UAV flight abilities, though in several cases the less processor-intensive surround method performed sufficiently better than the baseline to be considered useful in certain applications. The results from this project were utilised in a submitted, and accepted, peer-reviewed paper presented at the IEEE UEMCON 2020 conference