###### Abstract
For this task, many algorithms for visual tracking have been proposed, which can be generally categorized into generative and discriminative methods according to their appearance models. Generative methods typically search for the image region which is most similar to the target, such as incremental subspace tracker (IVT [27]), L1 tracker [28], real-time compressive sensing tracking (RTCST [29]), superpixel tracking [30], mean shift algorithm [31; 32; 33], and the continuously adaptive mean shift (CAMSHIFT) algorithm [34]. Discriminative methods or tracking-by-detection methods deal with the tracking problem as a binary classification task and separate the target object from the background, such as multiple instance learning (MIL) tracker [35], Struck [36], on-line AdaBoost (OAB) tracker [37], and Tracking-Learning-Detecting (TLD) [38]. The potential of correlation filters for visual tracking has aroused tremendous research interest because it reduces the overhead time through fast Fourier transformation (FFT) [39]. [PERSON] et al. [40] presented a minimum output sum of squared error (MOSSE) filter, which produced stable correlation filters when initialized using a single frame. It is able to quickly adapt to variations in scale, rotation, and lighting while operating at 669 frames per second. [PERSON] et al. [41] proposed the correlation filter of the circulant structure with kernel (CSK), and used the kernel trick to learn the appearance model. After that, the Kernelized Correlation Filter (KCF) tracker that [PERSON] proposed in [42] further improved the CSK method by using the histogram of oriented gradients (HOG) feature instead of gray feature to represent the object, which showed an amazing speed on the OTB2013 dataset [43]. However, the original KCF tracker uses a fixed size template and is not able to handle scale changes and occlusions, leading to bad performance in some scenarios. To alleviate theses drawbacks, [PERSON] et al. [44] proposed fast scalable solution based on KCF framework which used an adjustable Gaussian window function and a keypoint-based model for scale estimation to deal with the fixed size limitation in the Kernelized Correlation Filter. [PERSON] et al. (2019) presented an improved KCF tracker by adopting a cascade classifier composed of multi-scale correlation filter and NN (Neighbor Nearest) classifier, which showed favorable performance in accuracy and robustness. To actually track a moving target in real time with a vision-based system onboard a multi-rotor UAV platform, three steps are required. The first step is to detect the target and localize it in each image frame as described above. The second step involves getting the line-of-sight (LOS) angle or the position of the target and maintaining the target in the center of the camera's field of view (FOV). The third step is to use this information obtained by the vision system to define the control task of an autonomous UAV when flying around the target. Generally, a downward-facing strap-down camera has advantages with its small size, light weight, and simplicity ([PERSON] et al., 2019). However, the output of the strap-down camera cannot be directly used in the flight control system, for it couples with the UAV body angular motion. In addition, the restricted FOV of the camera makes it a challenging task to keep the fast-moving target in the image, which also means the FOV constraints must be fully considered in the guidance law ([PERSON] et al., 2019). To solve these problems, a gimbal system is widely used to provide inertial stability to the camera by isolating it from the UAV motion and vibration. A gimbaled camera now available on the market provides a decoupling along the roll, pitch, and yaw attitudes, which has become an important unit of the UAV system. In ([PERSON] et al., 2019), a 3-axis gimbaled camera (DJI Zenmuse X3) is used to solve the problem, which enabled autonomous landing of a quadrotor on a high-speed ground vehicle.