## 4 Results and Analysis  
### Ablation Study  
Our tracker involves three critical modules: multi-feature response fusion, subpixel localization, and motion trajectory correction and compensation. To evaluate their separate impact on the tracker's performance, we implement several variations of our tracker investigating the contribution of each module.  
The KCF tracker solely utilizing the multi-feature fusion strategy is denoted as KCF_MF, with CNN features as KCF_CNN, with subpixel localization as KCF_SL, with the adaptive Kalman filter as KCF_AKF, and with only the Kalman filter as KCF_KF. Finally, the proposed algorithm is denoted as Ours. The corresponding precision and success plots are illustrated in Figure 7 and Table 2, highlighting that the corresponding scores of the KCF_SL tracker are increased by 1% and 1.4%, respectively, compared to the KCF tracker. Although the scores are not significantly increased, the subpixel localization method manages to locate the object and improve the tracking performance accurately. The KCF_KF and KCF_AKF tracker increase the precision score by 5.8% and 1.8%, respectively, and the success score by 3.3% and 2.1%.  
Combining the KCF tracker and Kalman filter assists the tracker in correcting the tracking results and reduces the risk of drifting because the filter converges earlier and reduces tracking errors in time. The KCF_CNN and KCF_MF trackers also attain appealing tracking results increasing the precision score by 9.9% and 5.9%, respectively, and the success score by 6% and 4.4%. This performance enhancement is because the KCF_CNN tracker extracts the object's depth features, achieving appealing tracking results. Moreover, the KCF_MF tracker combines the HOG features and depth features to enhance the object's description further; thus, extracting multiple features from the object can improve tracking accuracy.  
Our tracker embedded with all three modules makes the tracker highly robust. The precision and success plots on the occluded videos are presented in Figure 8 and Table 3, highlighting that only our algorithm, KCF_AKF, and KCF_KF can solve the object occlusion problem, while the remaining algorithms perform poorly. This shows that both the adaptive Kalman filter and Kalman filter afford predicting the occluded object's position and continue to track it when the object occlusion ends.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Ours** & **KCF\_MF** & **KCF\_CNN** & **KCF\_SL** & **KCF\_AKF** & **KCF\_KF** & **KCF** \\ \hline AUC (\%) & **74.1** & 73.7 & 72.7 & 71.0 & 72.1 & 71.9 & 69.1 \\ \hline Precision score (\%) & **89.2** & 87.1 & 83.1 & 78.2 & 83.0 & 79.0 & 77.2 \\ \hline Success score (\%) & **97.2** & 96.4 & 94.8 & 91.8 & 93.7 & 92.5 & 90.4 \\ \hline FPS & 18 & 22 & 20 & 94 & 92 & 93 & **96** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The experimental ablation results of all un-occluded video sequences (in bold the optimal results per tracker).  
Figure 8: Ablation study on all the occluded video sequences. The legend in the precision and success plot are the precision and AUC value score per object tracker, respectively. (**a**) Precision plots; (**b**) Success plots.  
Figure 7: Ablation study on all the unoccluded video sequences. The legend in the precision and success plot are the precision and AUC value score per object tracker, respectively. (**a**) Precision plots; (**b**) Success plots.  
Based on the above analysis and tracking results, the three crucial modules proposed in this paper improve the object tracking performance. Our method can also be applied to other trackers for object tracking on the ground and obtain improved results.