# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Comparison and Analysis
#### 3.5.3 Hyperparameters Effects  
To validate the effectiveness of the occlusion-based key-frame detection algorithm proposed in our method and ensure that the method achieves optimal performance under the current parameter settings, the following experiment is designed. This experiment mainly adjusts three coefficients: the sensitivity coefficient used to determine whether the occluded area of the current frame has significantly decreased, the percentage threshold used to determine whether there is a significant change in the occluded area of the current frame, and the absolute threshold.  
**Sensitivity Coefficient**: This coefficient is used to determine whether the occluded area of the current frame has significantly decreased. It specifies the threshold reduction percentage between the occluded area of the current frame and the occluded area of the previous key frame, below which the current frame will be considered as a new key frame.  
**Percentage Threshold**: This threshold is used to determine whether the difference between the occluded area of the current frame and that of the previous key frame is significant. If the difference exceeds a certain percentage, then the current frame is considered as a new key frame.  
**Absolute Threshold**: Similar to the percentage threshold, the absolute threshold is used to determine whether the difference between the occluded area of the current frame and that of the previous key frame is significant. However, in this case, it is determined by the total sum of non-zero pixels in the difference image. If this sum exceeds a certain absolute threshold, then the current frame is considered as a new key frame.  
In the experimental setup, the three factors are condensed into two. The interaction between the percentage threshold and the absolute threshold is minimal. The design of two thresholds serves as a double insurance in the system, so the specific values for both thresholds can be considered as a single problem. Therefore, they are presented in the form of factor combinations. This design allows for the simultaneous consideration of multiple factors and levels, even within a relatively small experimental scale. With this design, each factor and its levels are adequately considered, enabling an effective exploration of the interactions between parameters. The system systematically tests the effects of different parameter combinations to find the optimal one. The experiment adjusts the parameters' sizes with a fixed step length, synchronously adjusting the absolute threshold and the percentage threshold. The results are shown in Table 4, where the horizontal direction represents different values of the sensitivity coefficient, and the vertical direction represents combinations of the percentage threshold and the absolute threshold.  
Figure 10: Comparison of tracking and reconstruction results for the _video_cup_ dataset. The first row shows the results obtained with BundleSDF, while the second row presents the output from our method.  
The experiment comprises a total of nine groups, evaluating the accuracy of the method under each parameter setting to ultimately determine the optimal parameter combination as (0.8, 0.05, 500), where the sensitivity coefficient is set to 0.8, the percentage threshold is 0.05, and the absolute threshold is 500. Since the experimental results are based on limited data, with image formats all being 480 \(\times\) 640 and most scenes being indoor environments, and with the target object occupying at least 20% of the frame, adjustments to the parameter settings should be made accordingly if these conditions change.