###### Abstract  
Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency. target tracking; UAV; onboard vision; gimbal system; low-cost sensors +
Footnote †: journal: Applied Sciences  
0000-0002-3000-0000-0000-0000-0000-0000-00000-0000-0000-0000-00000-00000-00  
most civilian used UAVs. As one of the most popular UAV platforms, quadrotors are more stable and have a lower manufacturing cost than helicopters. For portability and flexibility, the takeoff weight of most quadrotors is less than 15 kg, so payload and battery endurance for onboard equipment are very limited. In this situation, precisely and robustly tracking a moving target by using a small-scale UAV platform is still a challenging task because the onboard computational capability is poor and sensors are low-cost [17]. Compared with many other detection sensors, cameras seem to have an inherent potential for UAV-based target tracking tasks, for they passively receive the environmental information and visual features, while still being low-cost and lightweight [18]. In recent years, various methods are studied in many research works to achieve target tracking by using cameras. [PERSON] et al. [19] presented a visual tracking system for autonomous landing of a Hummingbird quadrotor by using an infrared (IR) camera that was extremely cheap. In [20], a vision-based landing control algorithm for an autonomous helicopter was designed and implemented by using a downward-pointing charge coupled device (CCD) camera. The onboard system was integrated with an algorithm for visual acquisition of the target (a moving helipad) and state estimation, which calculated the six degrees of freedom (DOF) pose with respect to the landing pad. The helipad which consists of a letter \"H\" surrounded by a circle is a typical structured pattern widely used in vision-based tracking and landing tasks because it can be easily detected and recognized in cluttered environments [21]. The research on square-based fiducial markers, such as AprilTag [22] and ArUco [23], has also aroused increasing interests toward marker-based visual tracking. Ref. [24] presented a fully autonomous flight control system for tasks of target recognition, geo-location, following and finally landing on a AprilTag marker which is attached to a high-speed moving car. Ref. [25] proposed a vision-based swarming approach for employing three or more Parrot Ar.Drone 2.0 quadrotors without any extra positioning sensors, while the ArUco markers fixed on the obstacles were used for localization and mapping. However, artificial markers or cooperative targets can only be applied to some specific scenarios such as autonomous landing and indoor navigation, in which the actual size and main features of the pattern are already known [26]. Since the vision system may be requested to track an arbitrary target with unknown size, shape, and motion, it is impossible to have already acquired information and know how to recognize it. Object tracking is one of the most fundamental fields in computer vision and has a wide range of applications in many areas.