## 4 Experiments
### Quantitative Evaluation  
We calculated the center location error (CLE) for quantitative performance evaluation; this means the euclidean distance between the center location of the ground truth and the estimated center location by the object tracker. The euclidean distance can be calculated as follows:  
\[CLE=\frac{1}{N}\sum_{n=1}^{N}\sqrt{(x_{b}^{n}-x_{g}^{n})^{2}+(y_{b}^{n}-y_{g}^{n })^{2}}. \tag{21}\]  
where \(x_{b}^{n}\) and \(x_{g}^{n}\) are the estimated center location by the tracker and \(x_{b}^{g}\) and \(x_{g}^{g}\) are the center location from the ground truth. \(N\) means the total number of pixels in the bounding box. If the center positions are close, the tracker can obtain a lower value, which means the good performance of the tracker. The precision was defined as success within 20 pixels (threshold), otherwise precision was defined as a failure. Thus, precision depended on the CLE results and its threshold. As another measurement, the success rate was defined as:  
\[S=\frac{|r_{t}\bigcap r_{a}|}{|r_{t}\bigcup r_{a}|}, \tag{22}\]  
where \(r_{t}\) and \(r_{a}\) are bounding boxes for the ground truth and estimated results by the tracking algorithm. \(\bigcap\) and \(\bigcup\) indicate intersection and union. The function \(|\ \cdot\ |\) means the number of pixels in the bounding box. The higher the success rate, the more overlap between the estimated bounding box and the ground truth. The success rate varied according to the threshold, and the threshold used in the experiment was 0.5. We can draw a success rate graph considering all thresholds and define the under area of the graph as the area under the curve (AUC). Then, we conducted experiments for all sequences in OTB-100. Table 1 shows the average performance evaluation results of proposed method with the state-of-the-art trackers. The scores of the proposed method were highest for all measurements. The precision score was higher than KCF [21] and STRUCK [5], and the success rate and AUC were higher than DSST [22]. Experimental results demonstrated the effectiveness of the proposed method compared to existing trackers. The results showed that the proposed tracker was suitable for sequences including a variety of environments. The proposed tracker can be applied to a variety of vision applications such as robotics, surveillance systems, motion analysis, autonomous cars, unmanned aerial vehicles (UAVs) and human computer interaction (HCI), as mentioned above. The precision plots and success plots for all experimental sequences with the proposed method and the state-of-the-art trackers are shown in Figure 5. The proposed method performed well across all thresholds, and the translation and scale estimation of the proposed method worked suitably. In particular, the success rate was generally higher than the precision, which means the estimated bounding box by our tracker was more overlapped with the bounding box from the ground truth. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trackers** & **CLE** & **Precision** & **Success Rate** & **AUC** \\ \hline STRUCK [5] & 47.09 & 0.6381 & 0.5046 & 0.4454 \\ MIL [6] & 71.96 & 0.4450 & 0.3132 & 0.3162 \\ TLD [9] & 60.14 & 0.5930 & 0.4819 & 0.4071 \\ FRAG [15] & 80.65 & 0.4245 & 0.3368 & 0.3182 \\ LSHT [37] & 68.24 & 0.4979 & 0.3742 & 0.3493 \\ KCF [21] & 44.88 & 0.7002 & 0.5252 & 0.4613 \\ DSST [22] & 56.47 & 0.6664 & 0.5738 & 0.4923 \\ Proposed & **43.50** & **0.7253** & **0.6485** & **0.5280** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The average performance evaluation results of trackers with the proposed method for 100 sequences in the OTB-100 database. The bold values mean the best performance. CLE, center location error. In addition, an experiment was conducted to combine components in various manners using KCF as a baseline algorithm. Each component was the partial blocks (PB), scale pyramid (SP), adaptive update (AU) and sparse update (SU). Figure 6 shows the precision plots and success plots for each combination.