## 1 Introduction  
Robust visual object tracking has been attracting substantial attention. It is a significant problem in computer vision, as evidenced by its numerous implementations in robotics, services, monitoring, and human-machine interaction. [PERSON] et al. [1] clearly defined computer vision as a key enabling technology for Industry 4.0. [PERSON] et al. [2] and [PERSON] et al. [3] showed the challenges and examples of computer vision technology in the field of robotics and human-robot collaboration. In typical scenarios, the target is specified in the first frame only (e.g., defining a rectangle), and it is often meaningful to track the target object in subsequent frames. This tracking can be directly applied in warehouse automation [4], human-robot handovers [5], safety design improvement for human-robot collaboration [4; 6; 7; 8; 9] and human-robot synchronization [10; 11]. However, many challenges are encountered in visually tracking an object, which are due to challenging factors such as deformation, illumination variation, scale variation, and partial occlusions. Most available methods that are used to solve visual tracking problems are based on two strategies: The first strategy is to use an efficient algorithm to construct generative [12; 13; 14] or discriminative [15; 16; 17] models. This strategy is commonly used to devise a filter or classifier for tracking the object and updating the model at each frame by utilizing the messages in subsequent frames as a training sample. It might lead to model shift because a small error can accumulate into a significant error when learning from predictions. Primarily, this strategy is applied in scenarios with a lack of training samples. The second type is used to exploit the features that are extracted from a deep convolutional neural networks (CNN) [18; 19; 20; 21], which is trained either online or on recognition datasets. Although these approaches can substantially improve the performance, the utilization of more complicated tracking algorithms or features would enormously increase the computational complexity, which might render the model unsuitable for real-time visual object tracking. Recently, many popular trackers [21; 22; 23; 24] that are based on correlation filters (CFs) have been proposed that can track many objects of interest because of their remarkable computational performance. By computing the correlation in the Fourier domain via the fast Fourier transform (FFT), the storage and computational requirements are both reduced by several orders of magnitude. [PERSON] et al. [23] proposed a minimum output sum of squared error (MOSSE) visual object tracking method that uses adaptive correlation filters to incorporate a correlation filter, which can outperform more complicated algorithms. Due to the high performance and efficiency of CF, many trackers have been designed by adopting MOSSE. [PERSON] et al. [22] proposed the circulant structure of tracking-by-detection which extends the dense sampling that is based on MOSSE and introduces the kernel trick (CSK). In 2015, [PERSON] et al. [25] put forward a solution for high-speed tracking with kernelized correlation filters (KCFs), extended the multiple feature channels, and improved the performance of the tracker by utilizing the histogram of oriented gradients (HOG) feature, while preserving the real-time performance. [PERSON] et al. [16] introduced the multiple feature channels of colour names (CNs), which are based on the CSK and have received an excellent response from the industry. However, although the trackers that are discussed above exhibit outstanding performance, they cannot solve the scale estimation problem due to the presence of fast motion or other factors. [PERSON] et al. [26] proposed an omnidirectional modified Laplacian operator with an adaptive window size. [PERSON] et al. [27] (DSST) solved the difficult scale estimation problem by learning discriminative correlation filters that are adopted from the scale pyramid representation. [PERSON] et al.