## 5 Experiments  
To evaluate the performance of our proposed tracking algorithm, we conduct an experiment on the OTB-13 [39] and OTB-15 [40] benchmark datasets. OTB is an authoritative benchmark that is used by many visual tracking researchers to evaluated the feasibility and efficiency of their proposed approaches. The evaluation methods for OTB-13 and OTB-15 are the same. OTB-13 has 50 sequences, while OTB-15 has 100 sequences. These sequences differ in terms of their challenging conditions; hence, they can be used to evaluate trackers more comprehensively. All test sequences of OTB have been tagged with 11 attributes which represent challenging conditions in various scenarios, such as background clutters (BC), motion blur (MB), illumination variation (IV), in-plane rotation (IPR), low resolution (LR), occlusion (OCC), out-of-plane rotation (OPR), scale variation (SV), deformation (DEF), fast motion (FM) and out-of-view (OV). Using these 11 metrics, we evaluate the performance of our approach under various scenarios. Furthermore, we utilize the metrics of OPE (one-pass evaluation) and SRE (spatial robustness evaluation). The success scores is the area under the curve (AUC), which describes the success rate when the estimated positions compare to the ground-truth positions with a fixed overlap threshold that ranges from 0 to 1. The precision scores mean is the percentage of the estimated centre positions that are within 20 pixels of the ground-truth centre positions. In this paper, we compare our algorithm with 7 state-of-the-art high-performance trackers on OTB-13 and OTB-15. We use the experimental results of 7 trackers that have been published by their authors to guarantee a fair comparison. Our algorithm is represented by Algorithm 1. In Table 1, we list important parameters that we used in our experiments. The values of these parameters is selected by conducting experiments with various parameter values to select the optimal values, which renders our approach more powerful. Our tracker is implemented in MATLAB on a Notebook with an Intel i5-6200U @2.3 GHz processor. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Learning rate(template) \(\eta_{Impl}\) & 0.02 \\ Learning rate (histogram) \(\eta_{hist}\) & 0.05 \\ Merge factor(template) \(\gamma_{Impl}\) & 0.70 \\ Merge factor(histogram) \(\gamma_{hist}\) & 0.30 \\ Colour features & RGB \\ \hline \hline \end{tabular}
\end{table} 
1:
2:Frame \(x_{t}\). Previous target object position \(p_{t-1}\) and scale \(s_{t-1}\). Previous template model \(A_{t-1}^{Impl}\), \(B_{t-1}^{Impl}\), histogram model \(\rho_{t-1}(O)\), \(\rho_{t-1}(B)\) and scale model \(A_{t-1}^{scale}\), \(B_{t-1}^{scale}\). 3:
4:Estimated target position \(p_{t}\) and scale \(s_{t}\). Updated template model \(A_{t}^{Impl}\), \(B_{t}^{Impl}\), histogram model \(\rho_{t}(O)\), \(\rho_{t}(B)\) and scale estimation model \(A_{t}^{scale}\), \(B_{t}^{scale}\). 5:repeat
6:Translation estimation:
7: Extract translation samples \(z_{trans}\) from \(x_{t}\) at \(p_{t-1}\) and \(s_{t-1}\). 8: Get the translation correlation \(f(x_{t})\) using \(z_{trans}\) with \(A_{t-1}^{Impl}\), \(B_{t-1}^{Impl}\) and \(\rho_{t-1}(O)\), \(\rho_{t-1}(B)\) in
9: Equations (4) and (5) Section 2.1 and combined these in Equation (4) Section 2.1. 10: Set \(p_{t}\) to the target position in the current frame that maximizes \(f(x_{t})\). 11:Scale estimation:
12: Extract scale samples \(z_{scale}\) from \(x_{t}\) at \(p_{t-1}\) and \(s_{t-1}\). 13: Get the scale correlation \(y_{scale}\) using \(z_{scale}\), \(A_{t-1}^{scale}\), \(B_{t-1}^{scale}\) in Equation (15) Section 2.2. 14:Set \(s_{t}\) to the target scale that maximizes \(y_{scale}\). 15:Model update:
16:Compute \(f_{max}\) and AFEPE in Equations (21) and (22) Section 4. 17:if\(f_{max}\) and AFEPE larger than historical mean values then
18: Update the translation model \(A_{t}^{Impl}\), \(B_{t}^{Impl}\) and \(\rho_{t}(O)\), \(\rho_{t}(B)\) in Equation (14\(b\)) and Equation (20\(b\)).