{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:59Z","timestamp":1760146679157,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFE0103800","2021YFB1407004"],"award-info":[{"award-number":["2023YFE0103800","2021YFB1407004"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Qinglan Project","award":["2023YFE0103800","2021YFB1407004"],"award-info":[{"award-number":["2023YFE0103800","2021YFB1407004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Tourists\u2019 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.<\/jats:p>","DOI":"10.3390\/ijgi13120423","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T11:25:33Z","timestamp":1732533933000},"page":"423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Passive Perception and Path Tracking of Tourists in Mountain Scenic Spots Through Face to Body Two Stepwise Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7136-5408","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"Jiangsu Key Laboratory of Regional Sustainable Development System Analysis and Simulation, 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":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"Jiangsu Key Laboratory of Regional Sustainable Development System Analysis and Simulation, 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\/0009-0006-1696-3612","authenticated-orcid":false,"given":"Kuntao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"Jiangsu Key Laboratory of Regional Sustainable Development System Analysis and Simulation, Jiangsu Normal University, Xuzhou 221116, 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":"Qian","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"Jiangsu Key Laboratory of Regional Sustainable Development System Analysis and Simulation, Jiangsu Normal University, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0394-7972","authenticated-orcid":false,"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"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xie, X., and Philips, W. (2017). Road intersection detection through finding common sub-tracks between pairwise GNSS traces. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6100311"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Popov, A.A., Li, Z., Hodgson, M.E., and Huang, B. (2024). A Sensor-Based Simulation Method for Spatiotemporal Event Detection. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13050141"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dong, W., Mao, X., Lu, W., Wang, J., and Cheng, Y. (2024). Construction and Inference Method of Semantic-Driven, Spatio-Temporal Derivation Relationship Network for Place Names. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13090327"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shi, K., Zhu, C., Li, J., Zhang, X., Yang, F., and Shen, Q. (2024). Spatiotemporal Information, Near-Field Perception, and Service for Tourists by Distributed Camera and BeiDou Positioning System in Mountainous Scenic Areas. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13100370"},{"key":"ref_5","unstructured":"Turk, M.A., and Pentl, A.P. (1991, January 3\u20136). Face recognition using eigenfaces. Proceedings of the 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Maui, HI, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/TNN.2003.813829","article-title":"Independent component analysis of Gabor features for face recognition","volume":"14","author":"Liu","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., and Liu, W. (2018, January 18\u201322). Cosface: Large margin cosine loss for deep face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00552"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., and Zafeiriou, S. (2019, January 16\u201320). Arcface: Additive angular margin loss for deep face recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102","DOI":"10.3103\/S0146411621010090","article-title":"MTCNN and FACENET based access control system for face detection and recognition","volume":"55","author":"Wu","year":"2021","journal-title":"Autom. Control Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Ververas, E., Kotsia, I., and Zafeiriou, S. (2020, January 14\u201319). Retinaface: Single-shot multi-level face localisation in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, M., Jain, A.K., and Liu, X. (2022, January 19\u201324). Adaface: Quality adaptive margin for face recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01819"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., and Liu, X. (2017, January 21\u201326). Disentangled representation learning GAN for pose-invariant face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.141"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, K., Rong, Y., and Li, C. (2018, January 18\u201322). Pose-robust face recognition via deep residual equivariant mapping. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00544"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Liu, T., Li, S., and Liu, W. (2020, January 23\u201328). Sub-center arcface: Boosting face recognition by large-scale noisy web faces. Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference on Computer Vision, Glasgow, UK. Part XI.","DOI":"10.1007\/978-3-030-58621-8_43"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s00371-020-01854-0","article-title":"Online multi-object tracking with pedestrian re-identification and occlusion processing","volume":"37","author":"Zhang","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1017\/ATSIP.2021.8","article-title":"The future of biometrics technology: From face recognition to related applications","volume":"10","author":"Imaoka","year":"2021","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"46","DOI":"10.18196\/jrc.v4i1.16808","article-title":"Smart attendance system based on improved facial recognition","volume":"4","author":"Dang","year":"2023","journal-title":"J. Robot. Control (JRC)"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, L., Hu, Y., Li, S., and Chen, L. (2016, January 11\u201314). MS-Celeb-1M: A dataset and benchmark for large-scale face recognition. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference on Computer Vision, Amsterdam, The Netherlands. Part III.","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., and Hongdong, L. (2016, January 27\u201330). Wider face: A face detection benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.596"},{"key":"ref_21","unstructured":"Milan, A., Leal-Taixe, L., Reid, I., Roth, S., and Schindler, K. (2016). MOT16: A Benchmark for Multi-Object Tracking. arXiv."},{"key":"ref_22","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Bu, J., and Tian, Q. (2015). Person Re-Identification Meets Image Search. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R., and Chang, Y. (2016, January 11\u201314). Performance measures and a data set for multi-target, multi-camera tracking. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., Li, X., and Yang, Y. (2018, January 18\u201322). Person transfer GAN to bridge domain gap for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00016"},{"key":"ref_25","unstructured":"Ge, Z., Liu, S., and Wang, F. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_26","first-page":"1415","article-title":"Fusion and visualization method of dynamic targets in surveillance video with geospatial information","volume":"48","author":"Zhang","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xie, Y., Wang, M., Liu, X., and Wu, Y. (2017). Integration of GIS and Moving Objects in Surveillance Video. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6040094"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xie, Y., Wang, M., Liu, X., and Wu, Y. (2019). Integration of Multi-Camera Video Moving Objects and GIS. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120561"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Batra, D., and Hoiem, D. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_30","first-page":"92","article-title":"Rapider-YOLOX: An efficient lightweight target detection network","volume":"5","author":"Gu","year":"2023","journal-title":"J. Intell. Sci. Technol."},{"key":"ref_31","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.","DOI":"10.3390\/electronics12040817"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., and Yang, B. (2021, January 11\u201317). Oriented R-CNN for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Faisal, M.M., Mohammed, M.S., Abduljabar, A.M., Abdulhussain, S.H., Mahmmod, B.M., and Khan, W. (2021, January 7\u201310). Object detection and distance measurement using AI. Proceedings of the 14th International Conference on Developments in eSystems Engineering (DeSE), Sharjah, United Arab Emirates.","DOI":"10.1109\/DeSE54285.2021.9719469"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: Challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_35","first-page":"13467","article-title":"Towards large-scale small object detection: Survey and benchmarks","volume":"45","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/12\/423\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:39:16Z","timestamp":1760114356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/12\/423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["ijgi13120423"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13120423","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2024,11,25]]}}}