# A Visual Object Tracking Algorithm Based on Improved TLD
## 4 Experiments and Analysis
### Tracking Accuracy and Robustness
#### 4.1.2 Experiments on OTB50  
Quantitative analysis for the proposed algorithm was obtained based on the full benchmark OTB50. The precision curve and success rate curve were chosen to evaluate the performance of the trackers. The left part of Figure 13 shows the precision curves, and the \(x\)-axis is the center location error (CLE), the Euclidean distance between the center of the target box and the ground truth. A frame \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Frames** & **TLD AOR** & **Our AOR** & **Growth Rate of AOR** \\ \hline Dudek & 1145 & 0.622 & 0.789 & 26.8\% \\ CarScale & 252 & 0.443 & 0.634 & 42.9\% \\ Redteam & 1918 & 0.582 & 0.759 & 30.4\% \\ Girl & 500 & 0.540 & 0.711 & 31.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average overlap rate (AOR) of video sequences. Figure 11: Bounding box overlap rate per frame in two sequences. can be considered correctly tracked if the CLE is within a distance threshold. The precision curve shows the percentage of correctly tracked frames for a range of distance thresholds. In the legend of the precision curves, the value in the square brackets is the precision value at a location threshold of 20 pixels. Similarly, the success rate curve presents the percentage of correctly tracked frames for a range of overlap rates. In the legend of success plots, the value in the square brackets is the success rate value in the threshold of 0.5. Figure 13: Cont. Figure 13 shows the tracking performance on the full dataset. The improved TLD was compared with the original TLD as well as other advanced tracking algorithms, including CSK, semi-supervised tracker (SemiT), tracking via sparsity-based collaborative model (SCM), and distribution fields for tracking (DFT) [23]. In addition to the average performance of the algorithm, performances in challenging scenes were also compared, including fast blur, occlusion, and in-plane rotation. It can be seen from Figure 13a,b that the improved TLD performed better than before. In Figure 13a, when the threshold was 20 pixels, the precision increased by 12.47%. In Figure 13b, when the threshold was 0.5, the success rate increased by 39.59%. In motion-blurred scenes, the tracking accuracy was greatly improved when the accuracy requirements were strict. For example, as shown in Figure 13c, when the threshold was 10, the accuracy of the TLD was 45%. Nevertheless, the improved TLD reached 80%. In the case of occlusion and in-plane rotation, our algorithm performed best, even exceeding KCF. This can be attributed to two reasons. For one thing, the addition of failure detection can help to avoid tracking the background. Also, it is the detection module of the TLD framework that detects and corrects the KCF tracker. Compared with other common algorithms on OTB, the performance of the improved TLD was excellent. In order to more intuitively observe the performance of different trackers in challenging scenes, five representative videos were selected for experiments. The basic information of these five groups of video sequences is shown in Table 2.  
Qualitative comparisons were carried out to compare tracking performances in challenging scenes from these sequences. As can be seen from Figure 14a that when the object was blocked, TLD contained too much background in its bounding box, resulting in losing track of the target in the following frames. Due to the object model, TLD tracked the object again after occlusion disappeared. However, CSK and