## 2 Literature Review
### Current Research Status of Computer Vision  
Computer vision is a technology that enables computers to acquire information, understand content, and make decisions from images or videos. By simulating the human visual system through algorithms, it processes, analyzes, and interprets visual data to perform tasks such as object recognition, motion detection, and 3D scene reconstruction. This technology is widely applied in autonomous driving, medical imaging, security surveillance, industrial inspection, and human-computer interaction, making it a crucial branch of artificial intelligence [18; 19; 20]. Currently, computer vision applications in tourist attractions primarily focus on safety early warning systems, smart tourism, and crowd density monitoring [21; 22; 23; 24]. For example, [PERSON] proposed an automated image analysis algorithm based on computer vision and artificial intelligence to detect anomalies in tourist scenes, providing a novel approach to ensuring the safety of visitors and facilities [25]. [PERSON] et al. employed a VGGT-Count network model to predict high-density crowd gatherings in real time, facilitating crowd control and flow management to mitigate safety risks [23]. [PERSON] et al. utilized Convolutional Neural Networks (CNNs) to analyze tourist flow patterns and preferences, achieving high accuracy in identifying abnormal situations within scenic areas [24]. In summary, computer vision technology has proven highly effective in improving safety management, visitor flow control, and intelligent services at tourist attractions. Through efficient image analysis and intelligent algorithms, it provides data-driven decision support for scenic area management. However, current research primarily focuses on optimizing general scenarios, with no existing studies applying computer vision to evaluate the age-friendliness of tourist facilities. As global population aging accelerates, the need for age-friendly modifications in tourist attractions becomes increasingly urgent. Present evaluation methods remain predominantly dependent on manual surveys, failing to incorporate the quantitative analysis of elderly visitors' actual behaviors and needs. Computer vision technology offers an effective solution to this gap by automatically capturing elderly tourists' behavioral data, enabling more efficient and objective assessments of facility age-friendliness. This innovative approach overcomes the limitations of traditional methods, such as cumbersome processes, low efficiency, and subjectivity, thereby providing scientifically grounded, data-driven insights for age-friendly renovations. This study required the identification and analysis of tourists, necessitating the use of object detection technology. Object detection is a core computer vision task that involves identifying and localizing specific objects within images or videos. Unlike simple image classification, object detection not only determines the presence of objects (e.g., people, vehicles, animals) but also precisely marks their locations with bounding boxes, along with their categories and confidence scores. This capability makes it suitable for multi-object recognition in complex scenes [26]. Traditional object detection methods, based on machine learning, involve feature extraction, feature selection, and classification [26]. These methods typically consist of three stages: (1) region selection using multi-scale sliding windows, which is computationally expensive and generates redundant regions; (2) feature extraction using handcrafted descriptors like Histogram of Oriented Gradients (HOG), Haar-like, or Scale-Invariant Feature Transform (SIFT), which struggle with object diversity due to background noise, lighting variations, and viewpoint changes; and (3) classification using algorithms like Adaboost. However, these methods suffer from inefficiency, complex manual feature design, limited adaptability, and cumulative errors from each stage [27].