# Deep Directional Network for Object Tracking
## 5 Experiment
### Evaluation of Object Tracking Benchmark (OTB)  
To analyze the overall performance of our algorithm, the one-pass evaluation (OPE) standard of OTB was used for a comparative evaluation.  
Figure 7 shows the evaluations of this algorithm and 7 recent excellent algorithms using the OPE standard. Subfigure (a) evaluates the tracking precision, and (b) evaluates the success rate. Figure 7 shows that the algorithm is ranked first in terms of precision and success rate, although it has only a small gap with the algorithm in the second place.  
Figure 6: Eight algorithms for tracking results of various tracking sequences. (**a**) Bird2 tracking result; (**b**) Bolt tracking result; (**c**) Couple tracking result; (**d**) Football1 tracking result; (**e**) Jogging tracking result; (**f**) Human3 tracking result.  
Table 2 shows the precision and the tracking speed denoted by FPS of recent high-performance deep learning-based tracking algorithms. It is observed that the deep learning-based tracking algorithms developed in recent years have high-performance precision but have struggled with slower tracking speeds. Although our algorithm is still not real-time, it has greatly improved the speed compared to that of other high-performance deep networks. Due to the sliding window mechanism of our algorithm, the number of samples is reduced. The amount of computation in the deep network is successfully reduced. Hence, the tracking speed is improved.  
Various algorithms will have different effects on videos with different challenges; hence, this paper will also analyze the effect of each algorithm on different challenge factors, as shown in Figure 8. The graph shows the analysis of the effect of the four challenge factors. The effect of each algorithm on the in-plane rotation sequence is shown in Figure 8a,b, in which (33) indicates that 33 video sequences in the experimental sequence belong to in-plane rotation. It is observed that our algorithm deals with in-plane rotation (shown in Figure 8a,b) and scale variation (shown in Figure 8c,d) with the highest precision and success rate. Considering the background clutter (shown in Figure 8e,f), our algorithm performs well in terms of precision and is ranked third by success rate. Each algorithm has its limitations. For motion-blur video sequences shown in Figure 8g,h, the tracking effect of our algorithm has changed dramatically; hence, our algorithm is not suitable for tracking objects in such videos.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracker** & **Precision** & **FPS** \\ \hline Ours & 0.898 & 6 \\ MDNet & 0.909 & \textless{}1 \\ CREST & 0.875 & 1 \\ C-COT & 0.824 & 1 \\ DeepSRDCF & 0.841 & \textless{}1 \\ CNT & 0.543 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision and speed comparison of six deep learning tracking algorithms.  
Figure 7: Precision and success plots on object tracking benchmark (OTB). (**a**) Precision plots of OPE; (**b**) Success plots of OPE.  
Figure 8: Performance evaluation of eight algorithms for different challenge factors. (**a**) Precision plots of OPE-in-plane rotation; (**b**) Success plots of OPE-in-plane rotation; (**c**) Precision plots of OPE-scale variation; (**d**) Success plots of OPE-scale variation; (**e**) Precision plots of OPE-background clutter; (**f**) Success plots of OPE-background clutter; (**g**) Precision plots of OPE-motion blur; (**h**) Success plots of OPE-motion blur.