## 5 Discussion  
### Comparison with Prior Work  
Traditional research on aging-friendly evaluations in tourist attractions primarily relies on data collection methods such as questionnaires, on-site observations, and random interviews. While these approaches can directly capture visitors' subjective feedback, they have inherent limitations. The data collection process demands significant human resources and time, resulting in low efficiency and difficulties in achieving large-scale, continuous monitoring. Additionally, survey outcomes are susceptible to sampling bias and respondents' subjective perceptions, potentially compromising data objectivity and representativeness. Furthermore, traditional methods struggle to capture dynamic changes in real time, such as fluctuations in facility usage or sudden issues, thereby limiting assessment accuracy and timeliness. This paper proposes a novel aging-friendly evaluation method that integrates multiple computer-vision-based object detection and recognition models. By calculating the spatiotemporal occupancy rates of rest facilities and the proportion of elderly users, it enables the efficient quantitative analysis of aging-friendly facility adequacy. As an intelligent monitoring tool, computer vision technology offers a new research paradigm for aging-friendly assessments in scenic areas. Leveraging algorithms such as image recognition and object detection, it automates data processing and facilitates the quantitative analysis of facility usage, characterized by high efficiency, objectivity, and dynamic monitoring capabilities. The proposed method significantly improves data processing and analysis efficiency while reducing labor costs. Moreover, objective monitoring minimizes subjective biases inherent in human evaluations. The real-time analytical capacity of computer vision also supports dynamic assessments of facility usage, enabling the better analysis of temporal variations in tourist areas. Thus, this approach addresses some shortcomings of traditional methods and provides scientific support for optimizing service facility allocation and aging-friendly renovations in tourist attractions. However, applying computer vision technology to aging-friendly evaluations still faces challenges. Algorithm accuracy depends on high-quality image data, and complex environmental factors (e.g., lighting variations) may impair recognition performance. Additionally, privacy concerns arise regarding visitor data, and the method has limitations in capturing subjective experiences and needs compared to traditional approaches. Therefore, it cannot entirely replace conventional methods. Future research should explore the integration and synergistic innovation of computer vision and traditional methods. This approach should retain traditional surveys on tourists' subjective satisfaction while incorporating objective quantitative analysis through computer vision, with a strong emphasis on privacy protection technologies, to establish a more comprehensive, accurate, and ethically compliant age-friendly assessment system for scenic areas. On one hand, traditional methods such as questionnaires and on-site interviews should be preserved to maintain their multidimensional measurement advantages in assessing elderly tourists' subjective experiences (e.g., psychological comfort, service satisfaction, and emotional needs). On the other hand, computer vision techniques--including object detection and recognition algorithms--should be introduced to enable the objective quantitative analysis of service facility usage. Throughout this process, privacy protection measures such as data anonymization and de-identification must be applied to ensure the security of sensitive information (e.g., facial images). Additionally, multimodal data fusion analysis can be employed to cross-validate subjective perception data obtained from traditional surveys with objective behavioral data extracted via computer vision.