{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:57:32Z","timestamp":1789750652365,"version":"4.0.1"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t              https:\/\/ror.org\/00hhkn466","doi-asserted-by":"publisher","award":["24K15108"],"award-info":[{"award-number":["24K15108"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Detection, tracking, and re-identification (ReID) of children wearing similar uniforms in a kindergarten environment is a very complex challenge for computer vision. Traditional surveillance systems or simple convolutional neural network (CNN) models often fail to distinguish children in crowds and occlusions. To address this challenge, this study proposes a novel hybrid framework combining YOLOv8 and Vision Transformer (ViT). Using YOLOv8 for detection and ViT for global feature extraction, we trained the model on a custom dataset of 31,521 images, achieving an overall accuracy of 93.75%, and the public benchmark MOT20 dataset of 28,630 images, achieving an overall accuracy of 96.02%. Our system showed remarkable success in tracking performance, where it achieved 86.7% MOTA and 99.7% IDF1 scores. This high IDF1 score proves that the model is highly effective in preventing identity switch. The main novelty of this study is the behavioral analysis of children beyond the boundaries of surveillance, where we measure walking distance and trajectory, and screen time. Finally, through cross-dataset comparison with the MOT20 public benchmark, we demonstrated that our proposed customized model is much more effective than current state-of-the-art methods in overcoming the domain gap in specific environments such as kindergarten.<\/jats:p>","DOI":"10.3390\/jimaging12040150","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T12:11:44Z","timestamp":1774872704000},"page":"150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatially Time-Based Robust Tracking and Re-Identification of Kindergarten Students: A Hybrid Deep Learning Framework Combining YOLOv8n and Vision Transformer (ViT)"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5745-4439","authenticated-orcid":false,"given":"Md. Rahatul","family":"Islam","sequence":"first","affiliation":[{"name":"Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 808-0196, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yui","family":"Kataoka","sequence":"additional","affiliation":[{"name":"Department of Child Education, Fukuoka Kodomo Junior College, Fukuoka 818-0197, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1675-1098","authenticated-orcid":false,"given":"Keisuke","family":"Teramoto","sequence":"additional","affiliation":[{"name":"Department of Creative Science, Faculty of Education, Aichi University of Education, Kariya 448-8542, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keiichi","family":"Horio","sequence":"additional","affiliation":[{"name":"Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 808-0196, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1136\/jech-2018-210436","article-title":"Mental health benefits of interactions with nature in children and teenagers: A systematic review","volume":"72","author":"Tillmann","year":"2018","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_2","first-page":"102","article-title":"Using nature and outdoor activity to improve children\u2019s health","volume":"40","author":"McCurdy","year":"2010","journal-title":"Curr. Probl. Pediatr. Adolesc. Health Care"},{"key":"ref_3","first-page":"3","article-title":"Does access to green space impact the mental well-being of children: A systematic review","volume":"37","author":"McCormick","year":"2017","journal-title":"J. Pediatr. Health Care"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Poulain, T., Sobek, C., Ludwig, J., Igel, U., Grande, G., Ott, V., Kiess, W., K\u00f6rner, A., and Vogel, M. (2020). Associations of Green Spaces and Streets in the Living Environment with Outdoor Activity, Media Use, Overweight\/Obesity and Emotional Wellbeing in Children and Adolescents. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17176321"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1056\/NEJMsr043743","article-title":"A potential decline in life expectancy in the United States in the 21st century","volume":"352","author":"Olshansky","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Malagoli, E., and Di Persio, L. (2025). 2D Object Detection: A Survey. Mathematics, 13.","DOI":"10.3390\/math13060893"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","article-title":"Deep Learning for Person Re-Identification: A Survey and Outlook","volume":"44","author":"Ye","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Zheng, L., Yang, Y., and Hauptmann, A.G. (2016). Person reidentification: Past, present and future. arXiv."},{"key":"ref_10","unstructured":"He, L., Liao, X., Liu, W., Liu, X., Cheng, P., and Mei, T. (2020). FastReID: A Pytorch Toolbox for General Instance Re-identification. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Trujillo-Lopez, L.A., Raymundo-Guevara, R.A., and Morales-Arevalo, J.C. (2025). User-Centered Design of a Computer Vision System for Monitoring PPE Compliance in Manufacturing. Computers, 14.","DOI":"10.20944\/preprints202506.2464.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, Q., Jiang, X., and Jiang, R. (2025). Classroom Behavior Recognition Using Computer Vision: A Systematic Review. Sensors, 25.","DOI":"10.3390\/s25020373"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yi, K., Li, J., and Zhang, Y. (2025). Multi-Object Tracking with Confidence-Based Trajectory Prediction Scheme. Sensors, 25.","DOI":"10.20944\/preprints202510.2145.v1"},{"key":"ref_14","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A Survey of Deep Learning-Based Object Detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cheng, G., Chao, P., Yang, J., and Ding, H. (2024). SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance. Appl. Sci., 14.","DOI":"10.3390\/app14125341"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., and Tian, Q. (2017, January 21\u201326). Person Re-identification in the Wild. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.357"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6893","DOI":"10.1109\/JIOT.2020.3036859","article-title":"CalibrationTalk: A Farming Sensor Failure Detection and Calibration Technique","volume":"8","author":"Lin","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Miao, J., Wu, Y., Liu, P., Ding, Y., and Yang, Y. (2019, January 27\u201329). Pose-Guided Feature Alignment for Occluded Person Re-Identification. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00063"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020). Identity-Guided Human Semantic Parsing for Person Re-identification. Computer Vision\u2013ECCV 2020, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-58542-6"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, L., Liang, J., Li, H., and Sun, Z. (2018, January 18\u201323). Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00739"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s40747-025-01853-5","article-title":"Multi-granularity feature intersection learning for visible-infrared person re-identification","volume":"11","author":"Chan","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., and Jiang, W. (2019, January 16\u201317). Bag of Tricks and a Strong Baseline for Deep Person Re-Identification. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., and Tian, Q. (2018, January 18\u201323). Person Transfer GAN to Bridge Domain Gap for Person Re-identification. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00016"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, J., Zhao, S., Li, S., Cheng, B., and Chen, J. (2024). Research on Person Re-Identification through Local and Global Attention Mechanisms and Combination Poolings. Sensors, 24.","DOI":"10.3390\/s24175638"},{"key":"ref_27","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bin Amir, S., and Horio, K. (2024). YOLOv8s-NE: Enhancing Object Detection of Small Objects in Nursery Environments Based on Improved YOLOv8. Electronics, 13.","DOI":"10.3390\/electronics13163293"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_30","unstructured":"Habock, U., Garoffolo, A., and Benedetto, D.D. (2021). Darlin: Recursive Proofs using Marlin. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Han, C., Koo, Y., Kim, J., Choi, K., and Hong, S. (2023). Wafer Type Ion Energy Monitoring Sensor for Plasma Diagnosis. Sensors, 23.","DOI":"10.3390\/s23052410"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021, January 10\u201317). CvT: Introducing Convolutions to Vision Transformers. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dai, S., Zhou, H., Han, T., Yang, M., and Zhang, Y. (2022, January 24\u201326). Blockchain-Based Smart Kitchen Platform for the Catering Industry. Proceedings of the 2022 5th International Conference on Hot Information-Centric Networking (HotICN), Guangzhou, China.","DOI":"10.1109\/HotICN57539.2022.10036214"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A Survey on Vision Transformer","volume":"45","author":"Han","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, C., Zhao, Z., Zhou, Y., Ma, L., Sui, X., Huang, Y., Yang, X., and Ma, X. (2024). Object Detection and Information Perception by Fusing YOLO-SCG and Point Cloud Clustering. Sensors, 24.","DOI":"10.3390\/s24165357"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/s11042-026-21267-w","article-title":"Robust vision transformer-based framework for person re-identification through occlusion-aware training","volume":"85","author":"Fouad","year":"2026","journal-title":"Multimed. Tools Appl."},{"key":"ref_37","first-page":"104","article-title":"Deep Affinity Network for Multiple Object Tracking","volume":"43","author":"Sun","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., and Claudel, C. (2020, January 13\u201319). Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"ref_39","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (July, January 26). Social LSTM: Human Trajectory Prediction in Crowded Spaces. Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103393","DOI":"10.1109\/ACCESS.2021.3098951","article-title":"A Trajectory-Driven 3D Channel Model for Human Activity Recognition","volume":"9","author":"Abdelgawwad","year":"2021","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","article-title":"FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., and Hassner, T. (2022). ByteTrack: Multi-object Tracking by Associating Every Detection Box. Computer Vision\u2013ECCV 2022; Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-031-19775-8"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cao, J., Pang, J., Weng, X., Khirodkar, R., and Kitani, K. (2023, January 17\u201324). Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., and Hassner, T. (2022). MOTR: End-to-End Multiple-Object Tracking with Transformer. Computer Vision\u2013ECCV 2022, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-19833-5"},{"key":"ref_45","first-page":"6504","article-title":"Hybrid-SORT: Weak cues matter for online multi-object tracking","volume":"38","author":"Yang","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Meng, W., Duan, S., Ma, S., and Hu, B. (2025). Motion-Perception Multi-Object Tracking (MPMOT): Enhancing Multi-Object Tracking Performance via Motion-Aware Data Association and Trajectory Connection. J. Imaging, 11.","DOI":"10.3390\/jimaging11050144"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Islam, R., and Horio, K. (2024, January 13\u201314). Identification of Human Activity by Utilizing YOLOv5s Approaches. Proceedings of the 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), Mount Pleasant, MI, USA.","DOI":"10.1109\/ICMI60790.2024.10586161"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bisla, T., Shukla, R., Dhawan, M., Islam, R., and Horio, K. (2024, January 12\u201314). Jumping behavior Analysis after Identification of Daycare Children\u2019s Activities utilizing YOLOv8 Algorithm. Proceedings of the 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT62155.2024.10677280"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7307","DOI":"10.3758\/s13428-024-02420-5","article-title":"Automatic object detection for behavioural research using YOLOv8","volume":"56","author":"Hermens","year":"2024","journal-title":"Behav. Res. Methods"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wu, Q., and Nie, X. (2025). Improved YOLOv10: A Real-Time Object Detection Approach in Complex Environments. Sensors, 25.","DOI":"10.3390\/s25226893"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, S., Luo, H., Wang, P., Wang, F., Li, H., and Jiang, W. (2021, January 10\u201317). TransRe-ID: Transformer-based Object Re-ID. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"222","DOI":"10.15359\/ru.39-1.13","article-title":"An Innovative Framework for Intelligent Computer Vision Empowered by Deep Learning","volume":"39","author":"Bikku","year":"2025","journal-title":"Uniciencia"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ghi\u021b\u0103, A.\u0218., and Florea, A.M. (2022). Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach. Sensors, 22.","DOI":"10.3390\/s22155856"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wu, R., and Qu, Z. (2023). A Cosine-Similarity-Based Deconvolution Method for Analyzing Data-Independent Acquisition Mass Spectrometry Data. Appl. Sci., 13.","DOI":"10.3390\/app13105969"},{"key":"ref_55","unstructured":"(2026, January 05). Available online: https:\/\/en.wikipedia.org\/wiki\/Euclidean_distance."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/4\/150\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T12:29:09Z","timestamp":1774873749000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/4\/150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":55,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["jimaging12040150"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12040150","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]}}}