## 1 Introduction  
Visual object tracking remains as an active research topic in computer vision that yields a variety of applications, such as intelligent video surveillance, human computer interaction, traffic control, and medical image analysis [1; 2; 3]. The goal of tracking is to track an arbitrary object throughout a video segment, given the initial location of the target object as a bounding box representation in the initial frame [4; 5]. Although numerous trackers have achieved promising results over the past decade, effective modeling of the appearance of tracked objects remains a challenging problem due to the real-time processing requirement and appearance changes caused by intrinsic and extrinsic factors [4; 6; 7] such as deformation, scale variations, in-plane and out-of-plane rotations, fast motion, illumination variation, background clutters, and occlusion. To solve the aforementioned challenges, many trackers have been proposed in the literature, which can be categorized into two classes according to the appearance representation scheme, i.e., generative methods and discriminative methods. Generative methods mainly concentrate on modeling the object appearance in the current frame and finding the image observation with maximal likelihood in the next frame. Such generative methods are usually built on templates matching [8], subspace learning [9], or sparse representations [10; 11]. These trackers mainly focus on exploiting foreground object information while ignoring the influence of related background information. In contrast, discriminative methods formulate visual object tracking as a binary classification problem, in which a classifier is trained to distinguish the foreground object from its surrounding background. Some researchers develop discriminative methods by modeling object appearance with multiple instance learning [12] or structure support vector machine (SVM) [13]. Furthermore, boosting [14], correlation filter [15; 16; 17], etc. are representative frameworks for designing a discriminative tracker. Recently, correlation filter has been a widely used framework for visual object tracking, due to its significant achievements and high computational efficiency. The earliest work was done by [PERSON] et al. [15], in which the correlation filter is learned by minimizing the total squared error between the actual output and the desired correlation output on a set of training images. Following this idea, many researches have been developed [16; 17; 18]. Representatively, [PERSON] et al. [18] proposed a kernelized correlation filter (KCF) based tracker by combining multi-channel features with kernel trick, which enhanced the robustness of existing correlation filter based trackers in dealing with challenging scenarios. Subsequently, many KCF-based trackers [19; 20] have been proposed to further improve the tracking performance in recent years. Although much progress has been made in existing state-of-the-art trackers, there are still some deficiencies to address. Firstly, because of the high sensitivity of correlation filter to occlusion, many correlation filter based trackers may easily drift to background and lead to tracking failure [21]. Secondly, the target object may be challenged by a variety of complicated intrinsic and extrinsic factors, such as scale variation, deformation, in-plane and out-of-plane rotations, and most existing correlation filter based trackers either use a sole filtering template or a fixed scale to represent the target object, these trackers cannot effectively capture appearance variations of target object [22; 23]. Thirdly, due to the high computational burden caused by modeling complex appearance models and estimating the scale of target object, most existing correlation filter based trackers could not run in real-time, which bring a lot of inconvenience in their applications [24; 25].