{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:23Z","timestamp":1760146343749,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T00:00:00Z","timestamp":1729382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB1407004","2023YFE0103800"],"award-info":[{"award-number":["2021YFB1407004","2023YFE0103800"]}]},{"name":"Jiangsu Qinglan Project","award":["2021YFB1407004","2023YFE0103800"],"award-info":[{"award-number":["2021YFB1407004","2023YFE0103800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijgi13100370","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T08:53:11Z","timestamp":1729500791000},"page":"370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spatiotemporal Information, Near-Field Perception, and Service for Tourists by Distributed Camera and BeiDou Positioning System in Mountainous Scenic Areas"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1696-3612","authenticated-orcid":false,"given":"Kuntao","family":"Shi","sequence":"first","affiliation":[{"name":"Jiangsu Provincial Key Laboratory for Regional Sustainable Development System Analysis and Simulation in Higher Education Institutions, Jiangsu Normal University, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4795-9975","authenticated-orcid":false,"given":"Changming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory for Regional Sustainable Development System Analysis and Simulation in Higher Education Institutions, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"State Key Laboratory of Remote Sensing Science, Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1054-5966","authenticated-orcid":false,"given":"Junli","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Institute of Xinjiang Ecology and Geography, Chinese Academy of Sciences, Urimuq 830011, China"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory for Regional Sustainable Development System Analysis and Simulation in Higher Education Institutions, Jiangsu Normal University, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2317-1705","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory for Regional Sustainable Development System Analysis and Simulation in Higher Education Institutions, Jiangsu Normal University, Xuzhou 221116, China"}]},{"given":"Qian","family":"Shen","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory for Regional Sustainable Development System Analysis and Simulation in Higher Education Institutions, Jiangsu Normal University, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00779-019-01341-x","article-title":"Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms","volume":"24","author":"Li","year":"2020","journal-title":"Pers. Ubiquit. Comput."},{"doi-asserted-by":"crossref","unstructured":"Liu, J., Du, J., Sun, Z., and Jia, Y. (2010, January 10\u201312). Tourism emergency data mining and intelligent prediction based on networking autonomic system. Proceedings of the 2010 International Conference on Networking, Sensing and Control (ICNSC), IEEE, Chicago, IL, USA.","key":"ref_2","DOI":"10.1109\/ICNSC.2010.5461495"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.22334\/jbhost.v6i2.235","article-title":"Tourist perception of visitor management strategy in North Bandung Protected Area","volume":"6","author":"Ervina","year":"2020","journal-title":"J. Bus. Hosp. Tour."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8239047","DOI":"10.1155\/2019\/8239047","article-title":"Applying big data analytics to monitor tourist flow for the scenic area operation management","volume":"2019","author":"Qin","year":"2019","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1080\/14724049.2021.1937189","article-title":"Managing the safety of nature Park visitor perceptions on risk and risk management","volume":"21","author":"Gstaettner","year":"2022","journal-title":"J. Ecotour."},{"doi-asserted-by":"crossref","unstructured":"Zhou, J. (2020, January 11\u201312). Design of intelligent scenic area guide system based on visual communication. Proceedings of the 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), IEEE, Vientiane, Laos.","key":"ref_6","DOI":"10.1109\/ICITBS49701.2020.00108"},{"key":"ref_7","first-page":"1","article-title":"Vision-based multiobject tracking through UAV swarm","volume":"20","author":"Shen","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"Hmidani, O., and Alaoui, E.M.I. (2022, January 12\u201314). A comprehensive survey of the R-CNN family for object detection. Proceedings of the 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), IEEE, Virtual.","key":"ref_8","DOI":"10.1109\/CommNet56067.2022.9993862"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.inffus.2021.10.013","article-title":"Multi-feature, multi-modal, and multi-source social event detection: A comprehensive survey","volume":"79","author":"Afyouni","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32650","DOI":"10.1109\/ACCESS.2021.3060821","article-title":"Analysis based on recent deep learning approaches applied in real-time multi-object tracking: A review","volume":"9","author":"Kalake","year":"2021","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Zheng, D., Dong, W., Hu, H., Chen, X., and Wang, Y. (2023, January 2\u20136). Less is more: Focus attention for efficient detr. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","key":"ref_12","DOI":"10.1109\/ICCV51070.2023.00614"},{"unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv.","key":"ref_13"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","key":"ref_14","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/978-3-030-01267-0_28","article-title":"Revisiting RCNN: On awakening the classification power of Faster RCNN","volume":"Volume 11219","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","key":"ref_16","DOI":"10.1109\/ICCV.2015.169"},{"doi-asserted-by":"crossref","unstructured":"Liu, B., Zhao, W., and Sun, Q. (2017, January 20\u201322). Study of object detection based on Faster R-CNN. Proceedings of the 2017 Chinese Automation Congress (CAC), IEEE, Jinan, China.","key":"ref_17","DOI":"10.1109\/CAC.2017.8243900"},{"unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO series in 2021. arXiv.","key":"ref_18"},{"unstructured":"Liu, W., Wen, Y., Yu, Z., and Yang, M. (2017). Large-margin softmax loss for convolutional neural networks. arXiv.","key":"ref_19"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"143378","DOI":"10.1109\/ACCESS.2021.3118048","article-title":"Video index point detection and extraction framework using custom YoloV4 Darknet object detection model","volume":"9","author":"Mahrishi","year":"2021","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","key":"ref_21","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_22","first-page":"11","article-title":"Visual object detection and tracking using YOLO and SORT","volume":"8","author":"Bathija","year":"2022","journal-title":"Int. J. Eng. Res."},{"doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","key":"ref_23","DOI":"10.1109\/ICIP.2017.8296962"},{"doi-asserted-by":"crossref","unstructured":"Parico, A.I.B., and Ahamed, T. (2021). Real time pear fruit detection and counting using YOLOv4 models and deep SORT. Sensors, 21.","key":"ref_24","DOI":"10.3390\/s21144803"},{"doi-asserted-by":"crossref","unstructured":"Chen, L., Ai, H., Zhuang, Z., and Shang, C. (2018, January 23\u201327). Real-time multiple people tracking with deeply learned candidate selection and person re-identification. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","key":"ref_25","DOI":"10.1109\/ICME.2018.8486597"},{"doi-asserted-by":"crossref","unstructured":"Wu, H., Du, C., Ji, Z., Gao, M., and He, Z. (2021). SORT-YM: An algorithm of multi-object tracking with YOLOv4-tiny and motion prediction. Electronics, 10.","key":"ref_26","DOI":"10.3390\/electronics10182319"},{"doi-asserted-by":"crossref","unstructured":"Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., and Yu, F. (2021, January 20\u201325). Quasi-dense similarity learning for multiple object tracking. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","key":"ref_27","DOI":"10.1109\/CVPR46437.2021.00023"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15380","DOI":"10.1109\/TPAMI.2023.3301975","article-title":"Qdtrack: Quasi-dense similarity learning for appearance-only multiple object tracking","volume":"45","author":"Fischer","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Valente, J., Van Der Voort, M., and Tekinerdogan, B. (2021). Effect of attention mechanism in deep learning-based remote sensing image processing: A systematic literature review. Remote Sens., 13.","key":"ref_30","DOI":"10.3390\/rs13152965"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-20047-2_1","article-title":"ByteTrack: Multi-object tracking by associating every detection box","volume":"Volume 13682","author":"Avidan","year":"2022","journal-title":"Computer Vision\u2014ECCV 2022"},{"unstructured":"Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., and Sun, J. (2018). Crowdhuman: A benchmark for detecting humans in a crowd. arXiv.","key":"ref_32"},{"unstructured":"Milan, A., Leal-Taixe, L., Reid, I., Roth, S., and Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv.","key":"ref_33"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2017, January 21\u201326). CityPersons: A diverse dataset for pedestrian detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_34","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional block attention module","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"unstructured":"Liu, S., Huang, D., and Wang, Y. (2019). Learning spatial fusion for single-shot object detection. arXiv.","key":"ref_36"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11310","DOI":"10.1364\/AO.412159","article-title":"Distorted pinhole camera modeling and calibration","volume":"59","author":"Zheng","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_38","first-page":"87","article-title":"A spatial localization method for dynamic objects in surveillance video","volume":"8","author":"Han","year":"2022","journal-title":"Survey. Mapp. Bull."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.inffus.2022.08.013","article-title":"Survey of landmark-based indoor positioning technologies","volume":"89","author":"Jang","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.inffus.2023.02.014","article-title":"Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes","volume":"95","author":"Jain","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13637","DOI":"10.1007\/s11042-022-13957-y","article-title":"Survey on algorithms of people counting in dense crowd and crowd density estimation","volume":"82","author":"Yang","year":"2023","journal-title":"Multimed. Tools Appl."},{"doi-asserted-by":"crossref","unstructured":"Yang, R., Li, W., Shang, X., Zhu, D., and Man, X. (2023). KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5. Electronics, 12.","key":"ref_42","DOI":"10.3390\/electronics12040817"},{"doi-asserted-by":"crossref","unstructured":"Specker, A., and Beyerer, J. (2023, January 18\u201322). ReidTrack: Reid-only Multi-target Multi-camera Tracking. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada.","key":"ref_43","DOI":"10.1109\/CVPRW59228.2023.00575"},{"key":"ref_44","first-page":"1","article-title":"A Review of the Development of Fusion Technology of Surveillance Videos and Geographic Information","volume":"5","author":"Han","year":"2022","journal-title":"Bull. Surv. Mapp."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3140033","article-title":"Thin cloud removal for remote sensing images using a physical-model-based CycleGAN with unpaired data","volume":"19","author":"Zi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, H., Jia, H., and Li, C. (2020, January 7\u201312). FFA-Net: Feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","key":"ref_46","DOI":"10.1609\/aaai.v34i07.6865"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/10\/370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:17:03Z","timestamp":1760113023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/10\/370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,20]]},"references-count":46,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["ijgi13100370"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13100370","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2024,10,20]]}}}