## 2 Related Works  
The existing trackers [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] have different approaches and can be classified according to the model. In recent years, the trackers have been divided into discriminative model-based [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 19; 20; 21; 22] and generative model-based [11; 12; 13; 14; 15; 16; 17; 18], and the proposed method belong to the discriminative model. Furthermore, some discriminative model-based trackers can be classified as correlation-based trackers [19; 20; 21; 22].  
The generation model-based trackers use a method of modeling the appearance of an object of interest and have various models for representing the object. Among generation model-based trackers, incremental visual tracking (IVT) [11] uses a PCA and applies an adaptive appearance update model to withstand lighting changes and variations. However, IVT is very sensitive to partial occlusion where the object is partially obscured by other objects. The occlusion problem was improved by applying the probability continuous outlier model (PCOM) [12] based on IVT. The visual tracking by decomposition (VTD) tracker [13] extended particle filter tracking (PFT), and the L1-minimization tracker [14] employed sparse representation. Furthermore, the fragment-based tracker (FRAG) [15] employed the local patch to ensure robustness for solving the partial occlusion problem, and the circulant sparse tracker (CST) [16] employed a combination of circularity and scarcity expressions. In addition, multi-task sparse learning tracker (MTT) [17] and the low-rank sparse trackers [18] belong to the generation model-based trackers.  
The discriminative model employs the method that classifies objects and background, and it learn the model directly. The ensemble trackers [1] proposed to combine multiple weak classifiers to form an ensemble structure. The online Ada boosting (OAB) [2] employed identifiable feature selection and online boosting, and the online random forest (ORF) [3] learned and classified random forests online. STRUCK [5], using the kernel [6], online support vector machine (SVM) [4] and the multiple instance learning (MIL) tracker with HAAR features also belong to the discriminative model. The weighted multiple instance learning (WMIL) [7] improved the weighting of positive samples in the MIL and reflected the weighting of samples when learning the classifier. The correlation filter-based tracker also belongs to the discriminative model. The minimizing the output sum of squared error (MOSSE) tracker [19] proposed an adaptive correlation filter, and circulant structure of circulant structure kernel (CSK) [20] used the dense sampling with the theory of circulant matrices and fast Fourier transform (FFT). Furthermore, the kernelized correlation filter (KCF) tracker [21] applied linear and kernel ridge regression with histogram of oriented gradients (HOG) features for high-speed tracking. However, the KCF tracker can only be used for object translation estimation. The scale estimation problem was solved by the discriminative scale space tracker (DSST) [22], which estimates the translation and scale, independently.