## 5 Conclusions  
This study introduced a VPS framework that integrates advanced object detection and segmentation technologies along with an innovative interaction module, which is designed to enhance augmented reality (AR) applications. The system efficiently processes user inputs to create precise masks, improving the overall AR experience. The SAM segmentation model, due to its prompt encoder and mask decoder, plays a crucial role in the VPS framework, allowing for rapid user input processing and accurate segmentation. Through experiments involving six different detectors, it was demonstrated that the combination of YOLO-NAS-L and SAM excelled over other configurations in terms of model size and \begin{table}
\begin{tabular}{l r r r r} \hline \hline  & **Original** & **Exception * ** & **Camel** & **Goldfish** \\ \hline DETR & 59.4 & 62.9 & 56.1 & 21.5 \\ DINO-scale4 & 61.0 & 62.1 & 98.3 & 41.1 \\ DINO-scale4-swin & 57.5 & 63.7 & 6.2 & 0.0 \\ DINO-scale5 & 64.7 & 64.2 & 98.3 & 62.9 \\ CoDETR-swin-o365 & 60.9 & 67.5 & 2.2 & 0.6 \\ CoDETR-swin-coco & 62.8 & 68.0 & 93.9 & 0.5 \\ CoDETR-F50 & 66.6 & 68.6 & 98.3 & 38.2 \\ CoDETR-deformable-detr-swin & 59.9 & 65.4 & 57.3 & 0.2 \\ YOLOv5-S & 61.6 & 65.1 & 98.3 & 16.4 \\ YOLOv5-M & 64.1 & 66.9 & 98.3 & 26.8 \\ YOLOv5-L & 65.8 & 67.3 & 98.3 & 43.8 \\ YOLOv8-S & 55.6 & 59.2 & 98.3 & 7.3 \\ YOLOv8-M & 65.0 & 67.0 & 98.3 & 35.5 \\ YOLOv8-L & 65.0 & 68.0 & 98.3 & 24.9 \\ YOLO-NAS-S & 68.5 & 70.5 & 98.3 & 39.6 \\ YOLO-NAS-M & 68.2 & 69.5 & 98.3 & 47.7 \\ YOLO-NAS-L & 70.0 & 71.0 & 97.3 & 53.3 \\ \hline Heuristic method & 84.2 & 83.5 & 98.3 & 89.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Swin-Transformer-based detectors exhibited subpar performance, particularly on the “camel” and “goldfish” sequences. Excluding these sequences led to a significant overall improvement in detection scores. Figure 10: A segment of the “camel” results for each model. In order from the top, we have the results for DINO-4 scale, DINO-4 scale-swin, and CoDETR-swin. The final line corresponds to the annotations originally provided by DAVIS. Frames without boxes or masks indicate that nothing has been detected in the image. processing speed. YOLO-NAS-L, in particular, achieved J- and F-scores of 70.0, outperforming DINO by 12.5%, as it scored 57.5%. Additionally, compared with traditional heuristic methods, where users manually select and segment objects at 1 FPS, our proposed VPS framework ensures smooth, uninterrupted user interaction, making it significantly more effective in terms of interface performance. Conceptualization, E.S. and B.-S.O. ; Software, D.O. ; Formal analysis, E.S. ; Data curation, D.O. ; Writing--original draft, E.S. and D.O. ; Writing--review & editing, B.-S.O. ; Supervision, B.-S.O. ; Funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Institute of Information and Communications Technology Planning and Evaluation (IITP) (grant number RS-2023-00227552). Not applicable. The DAVIS 2017 can be downloaded from the provider website: [[https://davischallenge.org/davis2017/code.html](https://davischallenge.org/davis2017/code.html)]([https://davischallenge.org/davis2017/code.html](https://davischallenge.org/davis2017/code.html)), accessed on 1 November 2024. This research was supported by a grant from the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean government (MSIT) (No.RS-2023-00227552, Development of artificial intelligence video background removal with SaaS using the domestic 64 TOPS semiconductor). Author [PERSON] was employed by the company Codevision Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: