# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 5 Results
### Qualitative Analysis  
In this section, we discuss selected qualitative tracking scenarios to verify the overall viability of the different methods. To illustrate the results, an in-depth look is provided by examining the variants of DiMP. For every figure, a closer look at the scenario is shown on the lower part of the figure, based on a region delimited by a red dash-dot rectangle in the corresponding upper image.  
Figures 16 and 17 display scenarios where the object is lost by the original DiMP variant in contrast to DiMP-2D and DiMP-3D. In the first scenario, the original variant, which uses a maximum-likelihood approach, misinterprets the distractor with the object because of the low number of pixels encoding the object. In contrast, by leveraging a particle filter, DiMP-2D and DiMP-3D handle the presence of distractors better and are less prone to fail on small-scale objects. The second scenario presents an occlusion situation, where the object is hidden by the tree in the roundabout. Both DiMP-2D and DiMP-3D recognize occlusion and can rely on their respective particle filter for predicting the position of the hidden object, until its reappearance.  
Figure 18 shows three consecutive frames where only DiMP-3D is unaffected by the ego-motion and can track the object robustly because the position of the object is expressed in the 3D scene space. Figure 19 illustrates another scenario where both DiMP-2D and DiMP-3D recognize the object as hidden, but while predicting the position of the hidden object; only DiMP-3D can predict robust and reasonable positions for the object even during camera motion. Relying on an image-based state estimator is only viable when ego-motion is very minimal, as in Figure 17. In Figure 20, solely DiMP-3D can identify the object undergoing occlusion, owing to the depth information leveraged by the depth map approximations in addition to the visual cues; whereas DiMP and DiMP-2D switch to a distractor because they solely rely on visual cues.  
Figure 16: Comparison of the evaluated trackers. DiMP-2D and DiMP-3D are able handle the presence of a distractors by taking advantage of the multimodal representation provided by a particle filter. In contrast, using the maximum-likelihood approaches, DiMP switches to a distractor.  
Figure 17: Comparison between the three DiMP variations. In this scenario, the 2D- and 3D-DiMP methods can handle the object undergoing occlusion.  
Figure 18: Comparison between DiMP-2D and DiMP-3D. During camera motion DiMP-2D is unable to track the object; whereas DiMP-3D is able to stabilize the tracking by expressing the object position in the 3D scene space. The light-blue grid is drawn to help visualize ego-motion.  
Despite DiMP-3D and ATOM-3D achieving remarkable results, there are cases where both fail. Occlusion on rare occasions is not identified correctly because of a strong distractor present when the object is partially hidden. This can be prevented by elaborating a different strategy for recognizing occlusions and reappearances of the object. Another point limiting the performance of DiMP-3D is illustrated in Figure 21, where the object slows down at the intersection for a long period. While the object is not moving, the particle filter continuously updates the estimated velocity to be adequate with the observations (velocity near zero). When the object accelerates, the particle filter cannot match the speed instantly, due to the transition model.  
Figure 19: Comparison between DiMP-2D and DiMP-3D. Only DiMP-3D is able to robustly track the object during ego-motion while the object is occluded. The light-blue grid is drawn to help visualize ego-motion.  
Figure 20: Comparison between DiMP variations. Owing to the depth information used for occlusion identification in addition to visual cues, DiMP-3D manages to handle the object undergoing occlusion; whereas DiMP and DiMP-2D switch to a distractor caused by relying solely on visual cues.