{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T17:50:31Z","timestamp":1771609831392,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100016833","name":"Yuncheng University","doi-asserted-by":"publisher","award":["YY-202312"],"award-info":[{"award-number":["YY-202312"]}],"id":[{"id":"10.13039\/100016833","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00521-025-11076-x","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T11:28:32Z","timestamp":1739964512000},"page":"9169-9192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Pedestrian mask-wearing detection based on YOLOv5 and DeepSORT"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8086-3378","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Abdul Samad","family":"Shibghatullah","sequence":"additional","affiliation":[]},{"given":"Kay Hooi","family":"Keoy","sequence":"additional","affiliation":[]},{"given":"Javid","family":"Iqbal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"11076_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.appet.2022.106127","volume":"176","author":"M Iranmanesh","year":"2022","unstructured":"Iranmanesh M, Ghobakhloo M, Nilashi M et al (2022) Impacts of the COVID-19 pandemic on household food waste behaviour: a systematic review. Appetite 176:106127. https:\/\/doi.org\/10.1016\/j.appet.2022.106127","journal-title":"Appetite"},{"key":"11076_CR2","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/S2213-2600(20)30134-X","volume":"8","author":"S Feng","year":"2020","unstructured":"Feng S, Shen C, Xia N et al (2020) Rational use of face masks in the COVID-19 pandemic. Lancet Respir Med 8:434\u2013436. https:\/\/doi.org\/10.1016\/S2213-2600(20)30134-X","journal-title":"Lancet Respir Med"},{"key":"11076_CR3","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.1109\/TMM.2021.3075566","volume":"24","author":"Z Shao","year":"2021","unstructured":"Shao Z, Cheng G, Ma J et al (2021) Real-time and accurate UAV pedestrian detection for social distancing monitoring in COVID-19 pandemic. IEEE Trans Multimed 24:2069\u20132083. https:\/\/doi.org\/10.1109\/TMM.2021.3075566","journal-title":"IEEE Trans Multimed"},{"key":"11076_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109207","volume":"125","author":"R Mar-Cupido","year":"2022","unstructured":"Mar-Cupido R, Garc\u00eda V, Rivera G, S\u00e1nchez JS (2022) Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19. Appl Soft Comput 125:109207. https:\/\/doi.org\/10.1016\/j.asoc.2022.109207","journal-title":"Appl Soft Comput"},{"key":"11076_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109933","volume":"134","author":"RAS Naseri","year":"2023","unstructured":"Naseri RAS, Kurnaz A, Farhan HM (2023) Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach. Appl Soft Comput 134:109933. https:\/\/doi.org\/10.1016\/j.asoc.2022.109933","journal-title":"Appl Soft Comput"},{"key":"11076_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109313","volume":"127","author":"M Gupta","year":"2022","unstructured":"Gupta M, Chaudhary G, Bansal D, Pandey S (2022) DTLMV2\u2014a real-time deep transfer learning mask classifier for overcrowded spaces. Appl Soft Comput 127:109313. https:\/\/doi.org\/10.1016\/j.asoc.2022.109313","journal-title":"Appl Soft Comput"},{"key":"11076_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10371-3","author":"S Wang","year":"2024","unstructured":"Wang S, Shibghatullah AS, Iqbal TJ, Keoy KH (2024) A review of multimodal-based emotion recognition techniques for cyberbullying detection in online social media platforms. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-024-10371-3","journal-title":"Neural Comput Appl"},{"key":"11076_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110682","volume":"146","author":"M Safaei","year":"2023","unstructured":"Safaei M, Soleymani SA, Safaei M et al (2023) Deep learning algorithm for supervision process in production using acoustic signal. Appl Soft Comput 146:110682. https:\/\/doi.org\/10.1016\/j.asoc.2023.110682","journal-title":"Appl Soft Comput"},{"key":"11076_CR9","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1109\/TMM.2020.3006415","volume":"23","author":"X Zhong","year":"2020","unstructured":"Zhong X, Huang P-C, Mastorakis S, Shih FY (2020) An automated and robust image watermarking scheme based on deep neural networks. IEEE Trans Multimed 23:1951\u20131961. https:\/\/doi.org\/10.1109\/TMM.2020.3006415","journal-title":"IEEE Trans Multimed"},{"key":"11076_CR10","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/3448250","volume":"64","author":"Y Bengio","year":"2021","unstructured":"Bengio Y, Lecun Y, Hinton G (2021) Deep learning for AI. Commun ACM 64:58\u201365. https:\/\/doi.org\/10.1145\/3448250","journal-title":"Commun ACM"},{"key":"11076_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106912","volume":"98","author":"MF Aslan","year":"2021","unstructured":"Aslan MF, Unlersen MF, Sabanci K, Durdu A (2021) CNN-based transfer learning\u2013BiLSTM network: a novel approach for COVID-19 infection detection. Appl Soft Comput 98:106912. https:\/\/doi.org\/10.1016\/j.asoc.2020.106912","journal-title":"Appl Soft Comput"},{"key":"11076_CR12","doi-asserted-by":"publisher","first-page":"30033","DOI":"10.1073\/pnas.1907373117","volume":"117","author":"TJ Sejnowski","year":"2020","unstructured":"Sejnowski TJ (2020) The unreasonable effectiveness of deep learning in artificial intelligence. Proc Natl Acad Sci 117:30033\u201330038. https:\/\/doi.org\/10.1073\/pnas.1907373117","journal-title":"Proc Natl Acad Sci"},{"key":"11076_CR13","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/j.ins.2020.09.003","volume":"546","author":"Y Ji","year":"2021","unstructured":"Ji Y, Zhang H, Zhang Z, Liu M (2021) CNN-based encoder-decoder networks for salient object detection: a comprehensive review and recent advances. Inf Sci 546:835\u2013857. https:\/\/doi.org\/10.1016\/j.ins.2020.09.003","journal-title":"Inf Sci"},{"key":"11076_CR14","doi-asserted-by":"publisher","first-page":"3540","DOI":"10.1109\/TNNLS.2019.2944979","volume":"31","author":"L Liu","year":"2020","unstructured":"Liu L, Zhang H, Xu X et al (2020) Collocating clothes with generative adversarial networks cosupervised by categories and attributes: a multidiscriminator framework. IEEE Trans Neural Netw Learn Syst 31:3540\u20133554. https:\/\/doi.org\/10.1109\/TNNLS.2019.2944979","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11076_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107331","volume":"105","author":"X Gao","year":"2020","unstructured":"Gao X, Zhang Z, Mu T et al (2020) Self-attention driven adversarial similarity learning network. Pattern Recognit 105:107331. https:\/\/doi.org\/10.1016\/j.patcog.2020.107331","journal-title":"Pattern Recognit"},{"key":"11076_CR16","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. https:\/\/doi.org\/10.48550\/ARXIV.1409.1556","DOI":"10.48550\/ARXIV.1409.1556"},{"key":"11076_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11076_CR18","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"11076_CR19","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 779\u2013788. https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"11076_CR20","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7263\u20137271. https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"11076_CR21","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. http:\/\/arxiv.org\/abs\/1804.02767"},{"key":"11076_CR22","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) YOLOv4: optimal speed and accuracy of object detection. http:\/\/arxiv.org\/abs\/2004.10934"},{"key":"11076_CR23","doi-asserted-by":"crossref","unstructured":"Zhu X, Lyu S, Wang X, Zhao Q (2021) TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 2778\u20132788","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"11076_CR24","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"11076_CR25","doi-asserted-by":"publisher","unstructured":"Wang C-Y, Yeh I-H, Liao H-YM (2024) YOLOv9: learning what you want to learn using programmable gradient information. https:\/\/doi.org\/10.48550\/arXiv.2402.13616","DOI":"10.48550\/arXiv.2402.13616"},{"key":"11076_CR26","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) SSD: Single Shot MultiBox Detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer Vision \u2013 ECCV 2016. Springer International Publishing, Cham"},{"key":"11076_CR27","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, et al (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"11076_CR28","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1109\/TMM.2021.3074273","volume":"24","author":"C Deng","year":"2021","unstructured":"Deng C, Wang M, Liu L et al (2021) Extended feature pyramid network for small object detection. IEEE Trans Multimed 24:1968\u20131979. https:\/\/doi.org\/10.1109\/TMM.2021.3074273","journal-title":"IEEE Trans Multimed"},{"key":"11076_CR29","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, et al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"11076_CR30","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"11076_CR31","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision. pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"11076_CR32","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11076_CR33","doi-asserted-by":"publisher","first-page":"5236","DOI":"10.3390\/s20185236","volume":"20","author":"B Qin","year":"2020","unstructured":"Qin B, Li D (2020) Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19. Sensors 20:5236. https:\/\/doi.org\/10.3390\/s20185236","journal-title":"Sensors"},{"key":"11076_CR34","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu L, Ouyang W, Wang X et al (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128:261\u2013318. https:\/\/doi.org\/10.1007\/s11263-019-01247-4","journal-title":"Int J Comput Vis"},{"key":"11076_CR35","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"11076_CR36","doi-asserted-by":"publisher","first-page":"19753","DOI":"10.1007\/s11042-021-10711-8","volume":"80","author":"S Singh","year":"2021","unstructured":"Singh S, Ahuja U, Kumar M et al (2021) Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimed Tools Appl 80:19753\u201319768. https:\/\/doi.org\/10.1007\/s11042-021-10711-8","journal-title":"Multimed Tools Appl"},{"key":"11076_CR37","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.neucom.2021.02.103","volume":"472","author":"Z Fan","year":"2022","unstructured":"Fan Z, Zhang H, Zhang Z et al (2022) A survey of crowd counting and density estimation based on convolutional neural network. Neurocomputing 472:224\u2013251. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.103","journal-title":"Neurocomputing"},{"key":"11076_CR38","doi-asserted-by":"publisher","unstructured":"Fang K, Xiang Y, Li X, Savarese S (2018) Recurrent autoregressive networks for online multi-object tracking. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, Lake Tahoe, NV, pp 466\u2013475. https:\/\/doi.org\/10.1109\/WACV.2018.00057","DOI":"10.1109\/WACV.2018.00057"},{"key":"11076_CR39","doi-asserted-by":"publisher","unstructured":"Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE, Beijing, pp 3645\u20133649. https:\/\/doi.org\/10.1109\/ICIP.2017.8296962","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"11076_CR40","doi-asserted-by":"publisher","first-page":"8725","DOI":"10.1109\/TMM.2023.3240881","volume":"25","author":"Y Du","year":"2023","unstructured":"Du Y, Zhao Z, Song Y et al (2023) StrongSORT: make DeepSORT great again. IEEE Trans Multimed 25:8725\u20138737. https:\/\/doi.org\/10.1109\/TMM.2023.3240881","journal-title":"IEEE Trans Multimed"},{"key":"11076_CR41","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wang C, Wang X et al (2021) FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int J Comput Vis 129:3069\u20133087. https:\/\/doi.org\/10.1007\/s11263-021-01513-4","journal-title":"Int J Comput Vis"},{"key":"11076_CR42","doi-asserted-by":"publisher","first-page":"40013","DOI":"10.1007\/s11042-022-12999-6","volume":"81","author":"JN Vibhuti","year":"2022","unstructured":"Vibhuti JN, Singh H, Rana PS (2022) Face mask detection in COVID-19: a strategic review. Multimed Tools Appl 81:40013\u201340042. https:\/\/doi.org\/10.1007\/s11042-022-12999-6","journal-title":"Multimed Tools Appl"},{"key":"11076_CR43","doi-asserted-by":"publisher","first-page":"218","DOI":"10.3390\/info14040218","volume":"14","author":"M Razzok","year":"2023","unstructured":"Razzok M, Badri A, El Mourabit I et al (2023) Pedestrian detection and tracking system based on Deep-SORT, YOLOv5, and new data association metrics. Information 14:218. https:\/\/doi.org\/10.3390\/info14040218","journal-title":"Information"},{"key":"11076_CR44","doi-asserted-by":"publisher","first-page":"15261","DOI":"10.1007\/s00521-023-08556-3","volume":"35","author":"M\u015e G\u00fcnd\u00fcz","year":"2023","unstructured":"G\u00fcnd\u00fcz M\u015e, I\u015f\u0131k G (2023) A new YOLO-based method for social distancing from real-time videos. Neural Comput Appl 35:15261\u201315271. https:\/\/doi.org\/10.1007\/s00521-023-08556-3","journal-title":"Neural Comput Appl"},{"key":"11076_CR45","doi-asserted-by":"publisher","first-page":"9895","DOI":"10.3390\/app13179895","volume":"13","author":"T Xie","year":"2023","unstructured":"Xie T, Yao X (2023) Smart logistics warehouse moving-object tracking based on yolov5 and deepsort. Appl Sci 13:9895. https:\/\/doi.org\/10.3390\/app13179895","journal-title":"Appl Sci"},{"issue":"1","key":"11076_CR46","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ad8a80","volume":"36","author":"S Wang","year":"2024","unstructured":"Wang S, Chen M, Ratnavelu K, Shibghatullah ASB, Keoy KH (2024) Online classroom student engagement analysis based on facial expression recognition using enhanced YOLOv5 for mitigating cyberbullying. Meas Sci Technol 36(1):015419. https:\/\/doi.org\/10.1088\/1361-6501\/ad8a80","journal-title":"Meas Sci Technol"},{"key":"11076_CR47","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:1904\u20131916. https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11076_CR48","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, et al (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"issue":"2","key":"11076_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2352\/J.ImagingSci.Technol.2024.68.2.020408","volume":"68","author":"R Zhu","year":"2024","unstructured":"Zhu R, Qi Y, Zhang Y (2024) Mask wearing detection for printing shop workers based on improved YOLOv5. J Imag Sci Technol 68(2):1\u201310. https:\/\/doi.org\/10.2352\/J.ImagingSci.Technol.2024.68.2.020408","journal-title":"J Imag Sci Technol"},{"key":"11076_CR50","doi-asserted-by":"publisher","first-page":"8768","DOI":"10.1038\/s41598-024-58800-6","volume":"14","author":"Y Liu","year":"2024","unstructured":"Liu Y, Jiang B, He H et al (2024) Helmet wearing detection algorithm based on improved YOLOv5. Sci Rep 14:8768. https:\/\/doi.org\/10.1038\/s41598-024-58800-6","journal-title":"Sci Rep"},{"key":"11076_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100881","volume":"23","author":"F Yu","year":"2023","unstructured":"Yu F, Zhang G, Zhao F et al (2023) Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms. Internet Things 23:100881. https:\/\/doi.org\/10.1016\/j.iot.2023.100881","journal-title":"Internet Things"},{"key":"11076_CR52","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, et al (2016) Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 3464\u20133468","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"11076_CR53","doi-asserted-by":"publisher","first-page":"23569","DOI":"10.1007\/s11042-022-14251-7","volume":"82","author":"ML Mokeddem","year":"2023","unstructured":"Mokeddem ML, Belahcene M, Bourennane S (2023) COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker. Multimed Tools Appl 82:23569\u201323593. https:\/\/doi.org\/10.1007\/s11042-022-14251-7","journal-title":"Multimed Tools Appl"},{"key":"11076_CR54","doi-asserted-by":"publisher","unstructured":"Punn NS, Sonbhadra SK, Agarwal S, Rai G (2021) Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. https:\/\/doi.org\/10.48550\/arXiv.2005.01385","DOI":"10.48550\/arXiv.2005.01385"},{"key":"11076_CR55","doi-asserted-by":"publisher","first-page":"6020","DOI":"10.1038\/s41598-024-56623-z","volume":"14","author":"C Huang","year":"2024","unstructured":"Huang C, Zeng Q, Xiong F, Xu J (2024) Space dynamic target tracking method based on five-frame difference and Deepsort. Sci Rep 14:6020. https:\/\/doi.org\/10.1038\/s41598-024-56623-z","journal-title":"Sci Rep"},{"key":"11076_CR56","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Liao H-YM, Wu Y-H, et al (2020) CSPNet: A new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops. pp 390\u2013391","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"11076_CR57","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci 2:160. https:\/\/doi.org\/10.1007\/s42979-021-00592-x","journal-title":"SN Comput Sci"},{"key":"11076_CR58","doi-asserted-by":"crossref","unstructured":"Jiang B, Luo R, Mao J, et al (2018) Acquisition of localization confidence for accurate object detection. In: Proceedings of the European conference on computer vision (ECCV). pp 784\u2013799","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"11076_CR59","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, et al (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"11076_CR60","doi-asserted-by":"crossref","unstructured":"Zheng Z, Wang P, Liu W, et al (2020) Distance-IoU loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI conference on artificial intelligence. pp 12993\u201313000","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"11076_CR61","unstructured":"Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention. Adv Neural Inf Process Syst 27. https:\/\/proceedings.neurips.cc\/paper\/5542-recurrent-models-of-visual-attention"},{"key":"11076_CR62","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11076_CR63","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on computer Vision and Pattern recognition. pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"11076_CR64","unstructured":"Liu Y, Shao Z, Teng Y, Hoffmann N (2021) NAM: Normalization-based Attention Module. http:\/\/arxiv.org\/abs\/2111.12419"},{"key":"11076_CR65","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"11076_CR66","doi-asserted-by":"crossref","unstructured":"Cao J, Pang J, Weng X, et al (2023) Observation-centric sort: Rethinking sort for robust multi-object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 9686\u20139696","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"11076_CR67","doi-asserted-by":"crossref","unstructured":"Ge S, Li J, Ye Q, Luo Z (2017) Detecting masked faces in the wild with lle-cnns. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2682\u20132690","DOI":"10.1109\/CVPR.2017.53"},{"key":"11076_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100881","author":"Z Wang","year":"2023","unstructured":"Wang Z, Huang B, Wang G et al (2023) Masked face recognition dataset and application. IEEE Trans Biom Behav Identity Sci. https:\/\/doi.org\/10.1016\/j.iot.2023.100881","journal-title":"IEEE Trans Biom Behav Identity Sci"},{"key":"11076_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103448","volume":"293","author":"W Luo","year":"2021","unstructured":"Luo W, Xing J, Milan A et al (2021) Multiple object tracking: a literature review. Artif Intell 293:103448. https:\/\/doi.org\/10.1016\/j.artint.2020.103448","journal-title":"Artif Intell"},{"key":"11076_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/0600000079","volume":"12","author":"J Janai","year":"2020","unstructured":"Janai J, G\u00fcney F, Behl A, Geiger A (2020) Computer vision for autonomous vehicles: Problems, datasets and state of the art. Found Trends\u00ae Comput Graph Vis 12:1\u2013308. https:\/\/doi.org\/10.1561\/0600000079","journal-title":"Found Trends\u00ae Comput Graph Vis"},{"key":"11076_CR71","doi-asserted-by":"publisher","unstructured":"Milan A, Leal-Taixe L, Reid I, et al (2016) MOT16: A Benchmark for Multi-Object Tracking. https:\/\/doi.org\/10.48550\/arXiv.1603.00831","DOI":"10.48550\/arXiv.1603.00831"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11076-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11076-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11076-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T08:48:12Z","timestamp":1746434892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11076-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,19]]},"references-count":71,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["11076"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11076-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,19]]},"assertion":[{"value":"17 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}