## 4 Experiments  
### Experimental Configuration  
Implementation details: The CFSA tracker is implemented in MATLAB on a regular PC with Intel i5-2450M CPU (2.50 GHz) processor, 4 GB memory and Debian 7.3 Operating System. In the proposed tracker, most of parameters are the same as the EdgeBoxes generator and the KCF tracker, the details are as follows. In the modified EdgeBoxes generator, the number of object proposals \(N\) is set to 5, the minimum box area of the object proposal is set to 0.7 times that of the target, \(\phi\) is set to 30 to control the speed that the score decays with the target scale, and the threshold \(\beta\) of IoU is set to 0.7. s\({}^{\text{d}}\) is set to 2 so that the modified EdgeBoxes generator can be performed on an image patch larger than the target. The learning rate \(\gamma\) and the Gaussian kernel parameter \(\sigma\) are set to 0.5 and 0.01, respectively. 11-channel color naming and 31-channel HoG features are used in our experiments, the size and the orientation number of HoG are 4\(\times\)4 and 9, respectively. The searching window used to train the discriminative correlation filter should be larger than the given target so that the size of searching window is set to 2.5 times that of the target. The threshold _Th_ that is used to determine whether the model is updated is set to 10 according to [15]. To estimate the scale of the target object, the mean of five scaling factors are used so that \(n\) is set to 4. All the above mentioned parameters are fixed throughout the experiments.  
Benchmark dataset: In this section, experiments are performed on two frequently used public benchmark datasets OOTB [18; 19; 20; 21] and OTB-100 [21; 25] to evaluate the efficacy of the CFSA tracker. These challenging sequences are classified into 11 attributes [2; 4], including deformation (DEF), occlusion (OCC), fast motion (FM), in-plane rotation (IPR), illumination variation (IV), out-of-view (OV), background clutters (BC), out-of-plane rotation (OPR), low resolution (LR), motion blur (MB), and scale variation (SV). One sequence may be annotated with many attributes, and some attributes occur more frequently than others, for example, OPR and IPR. The position and scale of the target are given in the ground truth of benchmark datasets for initialization and evaluation. Please refer to OOTB [2] and OTB-100 [4] datasets for more details.  
Evaluation methodology: In this paper, the results of one-pass evaluation (OPE) are shown. OPE means running the tracking algorithm throughout a test sequence with initialization from the ground truth position in the first frame and reporting the average precision and success rate [2]. The precision and success rate are two widely used evaluation metrics for quantitative analysis [2; 4]. The precision plot refers to the center location error (CLE), which is defined as the average Euclidean distance between the center locations of the tracked target object and the manually labeled ground truths. The success plot is defined as the bounding box overlap, and the overlap score can be computed with \(\text{S}\ =\ \frac{\left|\text{B}_{\text{t}}\cap\text{B}_{\text{g}} \right|}{\left|\text{B}_{\text{t}}\cup\text{B}_{\text{g}}\right|}\), where \(\text{B}_{\text{t}}\) denotes the bounding box of tracked result, \(\text{B}_{\text{g}}\) denotes the bounding box of ground truth, \(\mid\ \cdot\mid\) is the number of pixels of the regions, and \(\cap\) and \(\cup\) represent the intersection and union of two regions.