# A Visual Object Tracking Algorithm Based on Improved TLD
## 4 Experiments and Analysis
### Tracking Accuracy and Robustness
#### 4.1.2 Experiments on OTB50
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Frames**} & **Moving** & \multirow{2}{*}{**Occlusion**} & **Motion** & **Illum.** & **Scale** & **Similar** \\  & & **Camera** & & **Blur** & **Change** & **Change** & **Object** \\ \hline CarScale & 252 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) \\ Deer & 71 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) \\ Car1 & 1020 & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Trellis & 569 & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ CarDark & 389 & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The properties of the five videos. Figure 13: Precision and success plots on the object tracking benchmark (OTB): (**a**) precision plots of the one-pass evaluation (OPE), (**b**) success plots of OPE, (**c**) precision plots of fast motion, (**d**) success plots of fast motion, (**e**) precision plots of occlusion, (**f**) success plots of occlusion, (**g**) precision plots of in-plane rotation, and (**h**) success plots of in-plane rotation. DFT failed to track the real target. Ours performed best in this sequence, tracking accurately even when occlusion was severe. In Figure 13(a),\(c\), the common challenge depicted is scale change. Our algorithm had the best adaptability. In Figure 13(a), when the object became larger, VDT, SemiT, KCF, and Struck kept tracking a part of the object. In Figure 13(c), ours tracked for longest time with high accuracy; KCF was the second best. Because of the fixed box size, KCF could not adapt to the scale changes. Too much unrelated background was included in the bounding box when the object became smaller. Inappropriate sizes of the bounding box led to low tracking accuracy and even a lost target, as in the 792 nd frame in Figure 13(c). Ours performed well because it only passed the predicted location of the object from the tracking module to the detection module. The size of the bounding box was determined by the detection module and the scanning-window grid. Further, the failure detection helped to avoid tracking the background. If the detection module also does not find the object, the tracker will not output a bounding box and KCF will not update the filter template. TLD mistook the similarity as the object in Figure 13(c), while ours did not. It could be inferred that the tracking module of TLD lost the target, so the detection module tracked the similarity by mistake. However, our tracking module was more robust and tracked correctly; then, the detection module detected around the predicted location. Therefore, ours tracked successfully. Figure 13: A qualitative comparison of our approach with seven common trackers. The difficulty in Figure 14b is motion blur. Ours, CSK, and Struck tracked the object but others lost it. The common difficulties of Figure 14d,e are illumination changes and background clutter. Most trackers kept tracking the object but with different offsets. Both motion blur and illumination change will make the texture of an object surface change, while occlusion may block the feature points [18]. For TLD, the complex background weakened the function of the first-level variance classifier, which resulted in many calculations for the next two classifiers. Further, the change of the surface affected the extraction of feature points. Finally, TLD tracked at a low speed and with poor accuracy. DFT uses raw intensity and locates an object by local optimum search, which makes it easy to lose the object when there is background clutter [24]. Our, KCF, and Struck trackers use gradient features, thus tracking with strong robustness to brightness changes and motion blur.