## 2 Literature Review
### Current Research Status of Computer Vision
Since 2014, deep learning has revolutionized object detection, gradually replacing traditional methods as the mainstream paradigm in computer vision. Compared to handcrafted feature-based approaches--which are sensitive to lighting, occlusion, and complex scenes--deep learning models such as Convolutional Neural Networks (CNNs), Regions with CNN features (R-CNNs), Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO) offer significant advantages: (1) end-to-end training enables the automatic learning of hierarchical features, overcoming the subjectivity of manual design; (2) transfer learning on large-scale datasets (e.g., Common Objects in Context (COCO)) ensures robustness even with limited annotated data; and (3) modern architectures (e.g., Faster R-CNN, YOLO) unify object localization and classification, eliminating the computational overhead of sliding windows while enabling real-time detection. These models extract semantic features through convolutional layers and optimize parameters via loss functions, achieving superior accuracy and efficiency in complex environments [26,28]. Deep learning applications span robotics, medical diagnosis, street-view recognition, remote sensing, web services, and autonomous driving [29, 30, 31, 32, 33, 34, 35, 36]. For instance, [PERSON] et al. employed the YOLOv10 model based on the Transformer architecture for heavy equipment detection on construction sites [37]. [PERSON] et al. evaluated the effectiveness of image enhancement techniques in detecting non-protective personal equipment using the YOLOv8 model [38]. [PERSON] et al. applied a method based on transfer learning and deep learning for hyperspectral data processing [39] while [PERSON] et al. utilized YOLOv5-FF to detect floating objects on the surface of freshwater environments [40]. These studies fully demonstrated the outstanding generalization capability, autonomous feature learning advantages, and precise complex pattern recognition potential of deep learning in cross-domain applications, paving a broad path for the innovative development of computer vision technologies. In terms of hardware, many studies have focused on acceleration through optimized computational architectures and algorithm-hardware co-design to enhance real-time performance and energy efficiency. For instance, [PERSON] et al. achieved efficient YOLOv3-Tiny acceleration on Field Programmable Gate Arrays (FPGA), reaching 96.6 GOPS and 17.3 FPS with only 4.12 W power consumption [41]. [PERSON] et al. optimized the SSD with cyclic tiling and Advanced eXtensible Interface (AXI) data reordering, achieving 534.72 GOPS throughput on FPGAs [42]. [PERSON] et al. proposed a two-stage detection scheme combining lightweight CNNs and quantization, enabling on-chip computation with 98.01% accuracy on the Modified National Institute of Standards and Technology (MNIST) database [43]. [PERSON] et al. developed dedicated post-processing hardware to improve system efficiency and reduce resource pressure on the main accelerator [44]. These innovations further highlight the performance advantages of deep learning in object detection, enabling low-power, high-efficiency, and high-precision real-time inference on edge devices for diverse applications. Given deep learning's robustness in complex environments and efficient detection capabilities, this study adopted deep learning for the automatic detection of seated tourists in scenic areas. Tourist scenes often feature variable lighting, dense crowds, and frequent occlusions, which challenge traditional methods. Deep learning models, trained on large datasets and leveraging transfer learning, effectively address these challenges. Moreover, modern lightweight architectures can run efficiently on edge devices, with end-to-end training allowing the direct prediction of object categories and locations from input images--eliminating the need for manual feature engineering.