# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 5 Results
### Quantitative Results  
In this section, we use the following terms: (1) \"original\", which refers to the unmodified visual object tracker ATOM and DiMP presented in [27; 28]. (2) \"2D variant\", denoting ATOM and DiMP--i.e., ATOM-2D, DiMP-2D--coupled with a particle filter, working in the 2D image space. (3) Lastly, the \"3D variant\" refers to ATOM and DiMP--i.e., ATOM-3D, DiMP-3D--utilizing 3D information combined with a particle filter operating in this 3D scene space. Table 1 summarizes the final tracking F1-scores \(f_{\text{final}}\) of ATOM and DiMP for every variation. Best \(f_{\text{final}}\) on each sequence and for each variant are indicated in bold. Based on Table 1, we observe that the original variations of ATOM and DiMP attain the lowest scores and the least stable results. As stated before, the original methods are designed for short-term tracking from a ground-level perspective by relying on visual cues. The original variants have no specific module integrated for handling partial or full occlusions apart from using a similarity score map with a threshold. If the similarity score map has no peaks (under a set threshold) and is widely spread, the tracker is able to recognize that the object is missing but is not able to predict the next position. The original variation is also more prone to switching the object with a false association--i.e., the distractor--because of the maximum-likelihood approach. Since the original trackers rely only on the learned appearance model, they are extremely dependent on the number of pixels that encode the object. This results in the tracker losing most of the tracked object when they are described with a low amount of pixels, i.e., small-scale objects. ATOM-2D and DiMP-2D also recognize occlusion only through visual cues, achieved by setting a minimum required similarity score as a threshold. Occlusion is identified by obtaining a similarity score \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Variants**} & \multirow{2}{*}{**Tracker**} & \multicolumn{2}{c}{**Sequence 0**} & \multicolumn{2}{c}{**Sequence 1**} \\ \cline{3-6}  & & \(f_{\text{final}}\uparrow\) & **std \(\downarrow\)** & \(f_{\text{final}}\uparrow\) & **std \(\downarrow\)** \\ \hline \multirow{2}{*}{Original} & ATOM & 0.625 & 0.233 & 0.577 & 0.232 \\  & DiMP & 0.593 & 0.239 & 0.579 & 0.239 \\ \hline \multirow{2}{*}{2D Variant (ours)} & ATOM-2D & 0.641 & 0.223 & 0.605 & 0.242 \\  & DiMP-2D & 0.663 & 0.201 & 0.625 & 0.235 \\ \hline \multirow{2}{*}{3D Variant (ours)} & ATOM-3D & 0.684 & 0.193 & 0.691 & 0.179 \\  & DiMP-3D & 0.715 & 0.161 & 0.701 & 0.143 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Final tracking F1-scores \(f_{\text{final}}\) and standard deviation for every tracker variation evaluated on AU-AIR-Track. that is below the set threshold. During occlusion, the position of the object cannot be inferred visually but can be estimated (to some extent) through the predictions of the particle filter. Relying solely on visual cues for identifying occlusion is limited since the tracker potentially misinterprets a fast appearance change with an occlusion. Another limitation is that only occlusions without ego-motion can be handled because the particle filter estimates positions in the 2D image space. Overall, there is an increase in the tracking F1-score compared to the original variations, but this gain is essentially due to better recognizing and handling of distractors and small-scale objects, leveraged by a multimodal state estimator. With the multimodal property, we allow groups of particles to form where high responses in the similarity score map are found. The different groups of particles are clustered depending on their locations, enabling the 2D trackers to consider and distinguish the object and distractors. Whereas, the original trackers, utilizing a maximum-likelihood approach, can only handle the highest response in the similarity score map.