The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous scenic areas, addressing the challenges of real-time passive perception and safety management of tourists. The technical framework involves the following steps: Firstly, real-time video stream signals are collected from multiple cameras to create a distributed perception network. Then, the YOLOX network model is enhanced with the CBAM module and ASFF method to improve the dynamic recognition of preliminary tourist targets in complex scenes. Additionally, the BYTE target dynamic tracking algorithm is employed to address the issue of target occlusion in mountainous scenic areas, thereby enhancing the accuracy of model detection. Finally, the video target monocular spatial positioning algorithm is utilized to determine the actual geographic location of tourists based on the image coordinates. The algorithm was deployed in the Tianmeng Scenic Area of Yimeng Mountain in Shandong Province, and the results demonstrate that this technical system effectively assists in accurately perceiving and spatially positioning tourists in mountainous scenic spots. The system demonstrates an overall accuracy in tourist perception of over 90%, with spatial positioning errors less than 1.0 m and a root mean square error (RMSE) of less than 1.14. This provides auxiliary technical support and effective data support for passive real-time dynamic precise perception and safety management of regional tourist targets in mountainous scenic areas with no/weak satellite navigation signals.

Tourists’ near-field passive perception and identification in mountain areas faces challenges related to long distances, small targets, varied-pose scenarios, facial occlusion, etc. To address this issue, this paper proposes an innovative technical framework based on a face-to-body (F2B) two-step iterative method aimed at enhancing the passive perception and tracking of tourists in complex mountain environments by integrating and coordinating body features with facial features. The F2B technical framework comprises three main components: target feature acquisition, multi-feature coupled re-identification, and target positioning and tracking. Initially, the faces and bodies of tourists are extracted from real-time video streams using the RetinaFace and YOLOX models, respectively. The ArcFace model is then employed to extract the facial features of the target tourists, linking them with the faces detected by RetinaFace. Subsequently, a multi-feature database is constructed using the Hungarian algorithm to facilitate the automatic matching of the face and body of the same tourist. Finally, the Fast-ReID model and a spatial position algorithm are utilized for the re-identification of tourist targets and tracking their dynamic paths. Based on public and actual scene datasets, deployment and testing in the Yimeng Mountain Scenic Area have demonstrated that the accuracy index AP of the F2B model reaches 88.03%, with a recall of 90.28%, achieving an overall identification accuracy of approximately 90% and a false alarm rate of less than 5%. This result significantly improves the accuracy of SOTA facial recognition models in the complex environments of mountainous scenic spots. It effectively addresses the challenges associated with the low identification accuracy of non-cooperative targets in these areas through a ground video sensing network. Furthermore, it offers technical support for spatiotemporal information regarding near-field passive perception and path tracking of tourists in mountain scenic spots and showcasing broad application prospects.