{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T22:11:57Z","timestamp":1778019117871,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"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":["2018AAA0103001"],"award-info":[{"award-number":["2018AAA0103001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["U1813208"],"award-info":[{"award-number":["U1813208"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62173319"],"award-info":[{"award-number":["62173319"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62063006"],"award-info":[{"award-number":["62063006"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020B1515120054"],"award-info":[{"award-number":["2020B1515120054"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["JCYJ20200109115610172"],"award-info":[{"award-number":["JCYJ20200109115610172"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2018AAA0103001"],"award-info":[{"award-number":["2018AAA0103001"]}]},{"name":"National Natural Science Foundation of China","award":["U1813208"],"award-info":[{"award-number":["U1813208"]}]},{"name":"National Natural Science Foundation of China","award":["62173319"],"award-info":[{"award-number":["62173319"]}]},{"name":"National Natural Science Foundation of China","award":["62063006"],"award-info":[{"award-number":["62063006"]}]},{"name":"National Natural Science Foundation of China","award":["2020B1515120054"],"award-info":[{"award-number":["2020B1515120054"]}]},{"name":"National Natural Science Foundation of China","award":["JCYJ20200109115610172"],"award-info":[{"award-number":["JCYJ20200109115610172"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2018AAA0103001"],"award-info":[{"award-number":["2018AAA0103001"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["U1813208"],"award-info":[{"award-number":["U1813208"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62173319"],"award-info":[{"award-number":["62173319"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62063006"],"award-info":[{"award-number":["62063006"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020B1515120054"],"award-info":[{"award-number":["2020B1515120054"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["JCYJ20200109115610172"],"award-info":[{"award-number":["JCYJ20200109115610172"]}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["2018AAA0103001"],"award-info":[{"award-number":["2018AAA0103001"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["U1813208"],"award-info":[{"award-number":["U1813208"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["62173319"],"award-info":[{"award-number":["62173319"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["62063006"],"award-info":[{"award-number":["62063006"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["2020B1515120054"],"award-info":[{"award-number":["2020B1515120054"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20200109115610172"],"award-info":[{"award-number":["JCYJ20200109115610172"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, person-following robots have been increasingly used in many real-world applications, and they require robust and accurate person identification for tracking. Recent works proposed to use re-identification metrics for identification of the target person; however, these metrics suffer due to poor generalization, and due to impostors in nonlinear multi-modal world. This work learns a domain generic person re-identification to resolve real-world challenges and to identify the target person undergoing appearance changes when moving across different indoor and outdoor environments or domains. Our generic metric takes advantage of novel attention mechanism to learn deep cross-representations to address pose, viewpoint, and illumination variations, as well as jointly tackling impostors and style variations the target person randomly undergoes in various indoor and outdoor domains; thus, our generic metric attains higher recognition accuracy of target person identification in complex multi-modal open-set world, and attains 80.73% and 64.44% Rank-1 identification in multi-modal close-set PRID and VIPeR domains, respectively.<\/jats:p>","DOI":"10.3390\/s23020813","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T04:59:58Z","timestamp":1673413198000},"page":"813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Lightweight Multimodal Domain Generic Person Reidentification Metric for Person-Following Robots"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2744-1397","authenticated-orcid":false,"given":"Muhammad Adnan","family":"Syed","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Konka R&D Department, Konka Group Co., Ltd., Shenzhen 518053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2998-267X","authenticated-orcid":false,"given":"Yongsheng","family":"Ou","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"Konka R&D Department, Konka Group Co., Ltd., Shenzhen 518053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guolai","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.robot.2016.07.004","article-title":"Identification of a specific person using color, height, and gait features for a person following robot","volume":"84","author":"Koide","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103348","DOI":"10.1016\/j.robot.2019.103348","article-title":"Monocular person tracking and identification with on-line deep feature selection for person following robots","volume":"124","author":"Koide","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_3","unstructured":"Ghimire, A., Zhang, X., Javed, S., Dias, J., and Werghi, N. (2022). Robot Person Following in Uniform Crowd Environment. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, B., Peng, H., Fu, J., Wang, D., and Lu, H. (2021, January 10\u201317). Learning Spatio-Temporal Transformer for Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01028"},{"key":"ref_5","unstructured":"Bhat, G., Danelljan, M., Gool, L.V., and Timofte, R. (November, January 27). Learning Discriminative Model Prediction for Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M. (2019, January 15\u201320). ATOM: Accurate Tracking by Overlap Maximization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00479"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Arras, K.O., Mozos, O.M., and Burgard, W. (2007, January 10\u201314). Using Boosted Features for the Detection of People in 2D Range Data. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy.","DOI":"10.1109\/ROBOT.2007.363998"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Leigh, A., Pineau, J., Olmedo, N., and Zhang, H. (2015, January 26\u201330). Person tracking and following with 2D laser scanners. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139259"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, B.X., Sahdev, R., and Tsotsos, J.K. (2017, January 16\u201319). Person Following Robot Using Selected Online Ada-Boosting with Stereo Camera. Proceedings of the Conference on Computer and Robot Vision (CRV), Edmonton, AB, Canada.","DOI":"10.1109\/CRV.2017.55"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10514-014-9385-0","article-title":"Fast RGB-D people tracking for service robots","volume":"37","author":"Munaro","year":"2014","journal-title":"Auton. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhong, B., Li, G., Zhang, S., and Ji, R. (2020, January 13\u201319). Siamese Box Adaptive Network for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Flores, S., and Jost, J. (2022, January 1\u20133). Person Re-Identification on a Mobile Robot Using a Depth Camera. Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE), Anchorage, AK, USA.","DOI":"10.1109\/ISIE51582.2022.9831515"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1049\/ipr2.12062","article-title":"A robust tracking algorithm for a human-following mobile robot","volume":"15","author":"Tsai","year":"2020","journal-title":"IET Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1109\/JSYST.2019.2942953","article-title":"A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments","volume":"14","author":"Pang","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_15","first-page":"186","article-title":"Convolutional Channel Features-Based Person Identification for Person Following Robots","volume":"Volume 867","author":"Koide","year":"2018","journal-title":"International Conference on Intelligent Autonomous Systems"},{"key":"ref_16","first-page":"100058","article-title":"A person-following method based on monocular camera for quadruped robots","volume":"2","author":"Liu","year":"2022","journal-title":"Biomim. Intell. Robot."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Narayan, N., Sankaran, N., Setlur, S., and Govindaraju, V. (2018, January 18\u201322). Re-identification for Online Person Tracking by Modeling Space-Time Continuum. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00193"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nissimagoudar, P.C., Iyer, N.C., Gireesha, H.M., Pillai, P., and Mallapur, S. (2022). Multi-pedestrian Tracking and Person Re-identification. International Conference on Soft Computing and Pattern Recognition, Springer.","DOI":"10.1007\/978-3-030-96302-6_16"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neucom.2019.08.008","article-title":"A dual CNN\u2013RNN for multiple people tracking","volume":"368","author":"Babaee","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1109\/JIOT.2019.2954804","article-title":"REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking","volume":"7","author":"Neff","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fleuret, F., Shitrit, H.B., and Fua, P. (2014). Re-identification for Improved People Tracking. Person Re-Identification, Springer.","DOI":"10.1007\/978-1-4471-6296-4_15"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.neucom.2022.01.008","article-title":"Online multi-object tracking with unsupervised re-identification learning and occlusion estimation","volume":"483","author":"Liu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_23","unstructured":"Welsh, J.B. (2017). Real-Time Pose Based Human Detection and Re-identification with a Single Camera for Robot Person Following. [Ph.D. Thesis, University of Maryland]."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Thakran, A., Agarwal, A., Mahajan, P., and Kumar, S. (2022). Vision-Based Human-Following Robot. In Advances in Data Computing, Communication and Security. Springer.","DOI":"10.1007\/978-981-16-8403-6_41"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Algabri, R., and Choi, M.T. (2020). Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature. Sensors, 20.","DOI":"10.3390\/s20092699"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chebotareva, E., Hsia, K.H., Yakovlev, K., and Magid, E. (2020, January 15\u201318). Laser Rangefinder and Monocular Camera Data Fusion for Human-Following Algorithm by PMB-2 Mobile Robot in Simulated Gazebo Environment. Proceedings of the 15th International Conference on Electromechanics and Robotics \u201cZavalishin\u2019s Readings\u201d, Ufa, Russia.","DOI":"10.1007\/978-981-15-5580-0_29"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cond\u00e9s, I., and Ca\u00f1as, J.M. (2019). Person Following Robot Behavior Using Deep Learning. Advances in Physical Agents, Springer.","DOI":"10.1007\/978-3-319-99885-5_11"},{"key":"ref_28","first-page":"6359","article-title":"Human detection and following robot","volume":"9","author":"Anuradha","year":"2020","journal-title":"Int. J. Sci. Technol. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104075","DOI":"10.1016\/j.robot.2022.104075","article-title":"Person-Following Controller with Socially Acceptable Robot Motion","volume":"153","author":"Montesdeoca","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ifacol.2019.01.062","article-title":"Video-guided Camera Control for Target Tracking and Following","volume":"51","author":"Gemerek","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neucom.2021.09.054","article-title":"Cross-domain person re-identification with pose-invariant feature decomposition and hypergraph structure alignment","volume":"467","author":"Yan","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., and Wu, F. (2021, January 20\u201325). Diverse part discovery: Occluded person re-identification with part-aware transformer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00292"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., Ye, Q., Guoliang, D.W., Zheng, L., Ye, Q., Kang, G., Yang, Y., and Jiao, J. (2018, January 18\u201323). Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity 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.2018.00110"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., Li, S., and Yang, Y. (2018, January 18\u201323). Camera Style Adaptation 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.2018.00541"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jin, X., Lan, C., Zeng, W., Chen, Z., and Zhang, L. (2020, January 13\u201319). Style Normalization and Restitution for Generalizable Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00321"},{"key":"ref_36","unstructured":"Jia, J., Ruan, Q., and Hospedales, T.M. (2019, January 9\u201312). Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice. Proceedings of the British Machine Vision Conference (BMVC), Cardiff, UK."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, T., Jeong, M., Park, H., and Kim, C. (2021, January 20\u201325). Meta Batch-Instance Normalization for Generalizable Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00343"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dai, Y., Li, X., Liu, J., Tong, Z., and Duan, L.Y. (2021, January 20\u201325). Generalizable Person Re-identification with Relevance-aware Mixture of Experts. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01588"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., and Bai, X. (2019, January 15\u201320). Progressive Pose Attention Transfer for Person Image Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00245"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201312). MixConv: Mixed Depthwise Convolutional Kernels. Proceedings of the British Machine Vision Conference (BMVC), Cardiff, UK."},{"key":"ref_42","unstructured":"Zagoruyko, S., and Komodakis, N. (2016, January 19\u201322). Wide Residual Networks. Proceedings of the British Machine Vision Conference (BMVC), York, UK."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lan, C., Zeng, W., Jin, X., and Chen, Z. (2020, January 13\u201319). Relation-Aware Global Attention for Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00325"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, F., Zuo, W., Lin, L., Zhang, D., and Zhang, L. (2016, January 27\u201330). Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.144"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., and Huang, K. (2017, January 21\u201326). Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.145"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1109\/TCSVT.2021.3072171","article-title":"Grayscale Enhancement Colorization Network for Visible-infrared Person Re-identification","volume":"32","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, A., and Zheng, W.S. (2018, January 8\u201314). Adversarial Open-World Person Re-Identification. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_18"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, H., and Liu, S. (2021, January 20\u201325). Person Re-identification using Heterogeneous Local Graph Attention Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01196"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Choi, S., Lee, S., Kim, Y., Kim, T., and Kim, C. (2020, January 13\u201319). Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01027"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lu, Y., Wu, Y., Liu, B., Zhang, T., Li, B., Chu, Q., and Yu, N. (2020, January 13\u201319). Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01339"},{"key":"ref_55","unstructured":"Jocher, G. (2022, November 01). YOLOv5 by Ultralytics. Released date: 2020-5-29. Available online: https:\/\/github.com\/ultralytics\/yolov5."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/813\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:08Z","timestamp":1760119568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/813"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020813"],"URL":"https:\/\/doi.org\/10.3390\/s23020813","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}