## 2 Related Works  
In this section, we briefly review related works, with main focus on correlation filter based visual trackers, and object proposals generator that is commonly used for object detection and visual trackingBolme et al. [15] proposed a correlation filters based visual tracker, 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, and experimental results show a distinct advantage with high speed. After that, a set of correlation filter based trackers were proposed [16; 17; 18; 22; 28]. [PERSON] et al. [16] proposed a circulant structure with kernels (CSK) tracker, which trains a kernelized correlation filter by kernel regularized least squares with dense sampling and can track the target object at hundreds of frames-per-second. Following the above idea, [PERSON] et al. [17] integrated adaptive low-dimensional variant of color attributes into CSK tracker to promote the performance of the algorithm. In order to handle more challenging scenarios in real-time, the CSK tracker was further improved by allowing multiple channels HOG feature in [18]. To further improve the tracking performance in challenging scenarios, many researches have been developed [21; 28; 29]. [PERSON] et al. [21] developed an object tracker by estimating the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features and redetecting the target with an online learning method in the case of tracking drifts. [PERSON] et al. [29] proposed a robust tracker by fusing the frame level detection strategy of tracking, learning and detection with the systematic model update strategy of kernelized correlation filter tracker. However, these trackers may easily fail to estimate the scale of target object in the case of scale variations. To handle this problem, [PERSON] et al. [22] proposed to train a scale filter to estimate the scale of the target object. [PERSON] and [PERSON] [28] proposed a tracker to estimate the scale changes by applying filters at multiple resolutions, and the proposed tracker samples the target with different scales, and resizes the samples into a fixed size to compare with the learnt model at each frame. The goal of generating object proposals is to create a relatively small set of candidate bounding boxes that cover the objects in the image [26]. Many object proposal generators were proposed in recent years, such as EdgeBoxes [26], CPMC [30], Selective Search [31] and BING [32]. Specially, in order to improve the computational efficiency of object detection, these object proposal generators have been used in several state-of-the-art object detection algorithms [33; 34; 35]. However, most of existing trackers pay little attention to generating high-quality candidate proposals, which are important for improving tracking accuracy. Only few object tracking methods with object proposals [36; 37; 38; 39; 40] were proposed recently. [PERSON] et al. [36] presented the EBT tracker that searches for randomly moving objects in the entire image instead of a local search window with object proposals generated by EdgeBoxes method. [PERSON] et al. [37] proposed to train an object-adaptive objectness for each tracking task with BING generator. [PERSON] and [PERSON] [38] presented a novel tracking system based on edge-based object proposal and data association. [PERSON] et al. [39] presented an active discriminative co-tracker by integrating an information maximizing sampling paradigm into an adaptive tracking-by-detection algorithm, which further increases its robustness against various tracking challenges. Moreover, [PERSON] et al. [40] proposed a correlation based tracker, in which a detection proposal scheme is applied as a post-processing step to improve the tracker's adaptability to scale and aspect ratio changes. The tracker presented in this paper is substantially different from the above-mentioned trackers in two aspects.