## 6 Conclusions  
In this study, we evaluated various depth extraction methods PbyP, CoRDE, CeRDE, and GCRDE across different object categories (person, bicycle, car, bus, and truck) and occlusion levels (0 to 3). The results reveal several important findings about the performance of these methods in terms of extraction time, accuracy, and RMSE. Regarding extraction time, bounding box-based methods (PbyP and CoRDE) consistently demonstrated slower extraction times compared to segmentation mask methods (CeRDE and GCRDE). This trend was evident across all object categories. While PbyP and  
Figure 35: (**a**) Truth Table results. (**b**) Complete Region Depth Extraction (CoRDE) results. (**c**) Central Region Depth Extraction (CeRDE) result. (**d**) Grid Central Region Depth Extraction (GCRDE) result. The red frame highlights the closest object. CoRDE took longer to extract depth, CeRDE and GCRDE were generally faster. These results indicate that GCRDE is more efficient in terms of accuracy compared to the segmentation mask methods, while CeRDE strikes a balance between speed and accuracy, making it suitable for applications requiring rapid depth extraction. In terms of accuracy, segmentation mask-based methods, especially CeRDE and GCRDE, outperformed other approaches for most object categories and occlusion levels. Moreover, segmentation mask methods showed superior performance in more complex objects like trucks and cars, whereas bounding box methods struggled, particularly at higher occlusion levels. GCRDE with segmentation masks achieved the highest accuracy in the person, bicycle, car, and truck categories, even at higher occlusions. This suggests that segmentation masks are more effective for precise depth estimation, providing higher accuracy despite extracting fewer depth points. RMSE analysis further supports the superior accuracy of segmentation mask methods. Both CeRDE and GCRDE provided lower RMSE values across object categories, indicating more precise depth estimations. In particular, for larger objects like trucks and buses, segmentation mask methods yielded significantly lower RMSE values, highlighting their ability to handle complex object shapes more effectively than bounding box methods, which showed higher RMSE values, particularly for larger or more occluded objects. Overall, the findings highlight a trade-off between speed and accuracy. Bounding box methods (PbyP and CoRDE) are less efficient in terms of speed and may not be ideal for applications requiring fast depth extraction. However, segmentation mask methods (CeRDE and GCRDE) excel in accuracy, making them ideal for applications where precise depth estimation is crucial, even if they come with a higher computational cost. These results suggest that the choice of method should depend on the specific requirements of the application, with CeRDE standing out as a well-rounded option that offers a strong balance of speed, accuracy, and low RMSE. Its ability to maintain high performance across various object categories and occlusion levels makes it particularly suitable for real-time applications where both computing efficiency and depth estimation precision are critical. CeRDE's consistent results show its adaptability, making it an ideal choice for scenarios involving dynamic environments, such as autonomous driving, robotics, and ADAS systems. Even though the proposed methods demonstrate promising results across different object categories and occlusion levels, this study has several limitations. One potential direction involves expanding the dataset to incorporate a larger quantity and variety of objects. In this study, sometimes obstacles such as trees, utility poles, motorcycles, and traffic cones block in front of annotated objects. These obstacles are not detected by the object detection method and are instead grouped with the target object. This causes instability and inaccurate depth estimates in applications.