## 1 Introduction  
Visual object tracking is a crucial research issue in computer vision and has many applications including video security, traffic monitoring, robotics and human computer interface. In the past decade, great improvements have been made by some visual tracking algorithms [1; 2; 3; 4; 5; 6], meanwhile large visual tracking datasets and benchmarks, such as the Object Tracking Benchmark (OTB)-50 [7], OTB-100 [8], Visual Object Tracking (VOT) [9; 10], Need for Speed (NfS) [11] and Multi-Object Tracking (MOT) [12] have sparked the interest of numerous scholars and given impetus to the research area appreciably. In spite of having made considerable headway in recent decades, the tracking issue is still considered a big challenge in some scenarios such as illumination variation, scale variation, occlusion, fast motion, deformation, background clutters, etc. Lately, correlation filter (CF) based methods [13; 14; 15] have been sought in visual tracking based on their excellent performance. CF-based trackers learn a correlation filter with training samples obtained by cyclically shifting base samples. For each new frame, the trained filter locates to the new target position where has the maximum response. At last, the filter is updated by the new location. By utilizing the circulant construction of training samples, CF-based trackers enable efficient learning and detecting in the Fourier domain. Despite their appealing performance both in accuracy and high frames per second (FPS) rate, there are some drawbacks that reduce the performance of CF-based trackers. A major drawback is the boundary effect caused by circularly shifting. To inhibit the boundary effect, a cosine window is usually employed. But the cosine window limits search areas and thereby the quantity of samples, specifically the quantity of negative samples, is sharply reduce. Therefore, CF-based trackers usually have insufficient context and thus readily drift in scenes of fast motion or background clutter. Moreover, CF-based trackers only learn a holistic model so that they cannot deal with cases of partial occlusion well. To further improve the performance of CF-based trackers, several effective methods have been proposed. By handling large scale changes in complex image sequences, the Scale Adaptive With Multiple Features Tracker (SAMF) [16], and the Discriminative Scale Space Tracker (DSST) [17] have achieved state of the art performance. Sum of Template and Pixel-wise LEarners (Staple) trackers [18] have learned a filter to enhance the robustness of color variations and distortions by combining two notations easily affected by complementary factors. However, these trackers have not more effective approaches to mitigate the boundary effect. The Spatially Regularized Discriminative Correlation Filters (SRDCF) tracker [19] induces boundary effects by adopting spatially regularized elements in training to punish correlation modulus according to their position. However, solving this optimization problem of this strategy is time-consuming. Lately, context-aware (CA) [20] and background-aware [21] CF-based trackers have demonstrated notable enhancement on visual tracking performance. These methods incorporate global context information into the filter training to add number of negative samples, the quantity of negative samples will obviously affect tracking results. However, there are two major problems: (1) context patches are simply selected on fixed positions around the target, which causes the problem that these selected negative samples are perhaps non-representative; and (2) only a holistic model is learned, these trackers lack robustness against partial occlusion. To tackle the above inherent problems, we propose an adaptive context-aware and structural correlation filter for visual tracking from the following three perspectives. Firstly, in order to add informative negative samples to inhibit the boundary effect caused by circularly shifting, we introduce a novel context selecting strategy to obtain negative samples.