{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T02:43:34Z","timestamp":1790995414425,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24992852"],"award-info":[{"award-number":["BR24992852"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Population growth and expansion of urban areas increase the need for the introduction of intelligent passenger traffic monitoring systems. Accurate estimation of the number of passengers is an important condition for improving the efficiency, safety and quality of transport services. This paper proposes an approach to the automatic detection and counting of people using computer vision and deep learning methods. While YOLOv8 and DeepSORT have been widely explored individually, our contribution lies in a task-specific modification of the DeepSORT tracking pipeline, optimized for dense passenger environments, strong occlusions, and dynamic lighting, as well as in a unified architecture that integrates detection, tracking, and automatic event-log generation. Our new proprietary dataset of 4047 images and 8918 labeled objects has achieved 92% detection accuracy and 85% counting accuracy, which confirms the effectiveness of the solution. Compared to Mask R-CNN and DETR, the YOLOv8 model demonstrates an optimal balance between speed, accuracy, and computational efficiency. The results confirm that computer vision can become an efficient and scalable replacement for traditional sensory passenger counting systems. The developed architecture (YOLO + Tracking) combines recognition, tracking and counting of people into a single system that automatically generates annotated video streams and event logs. In the future, it is planned to expand the dataset, introduce support for multicamera integration, and adapt the model for embedded devices to improve the accuracy and energy efficiency of the solution in real-world conditions.<\/jats:p>","DOI":"10.3390\/jimaging12010027","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T15:28:57Z","timestamp":1767626937000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Vision-Based People Counting and Tracking for Urban Environments"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1073-4254","authenticated-orcid":false,"given":"Daniyar","family":"Nurseitov","sequence":"first","affiliation":[{"name":"KazMunayGas Engineering LLP, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2869-772X","authenticated-orcid":false,"given":"Kairat","family":"Bostanbekov","sequence":"additional","affiliation":[{"name":"KazMunayGas Engineering LLP, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2661-8661","authenticated-orcid":false,"given":"Nazgul","family":"Toiganbayeva","sequence":"additional","affiliation":[{"name":"Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1091-8483","authenticated-orcid":false,"given":"Aidana","family":"Zhalgas","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cSmart City\u201d, Astana IT University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6343-5277","authenticated-orcid":false,"given":"Didar","family":"Yedilkhan","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cSmart City\u201d, Astana IT University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0355-5856","authenticated-orcid":false,"given":"Beibut","family":"Amirgaliyev","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cSmart City\u201d, Astana IT University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"ref_1","unstructured":"United Nations, D.o.E., and Social Affairs, P.D. (2019). World Urbanization Prospects 2018: Highlights, United Nations. ST\/ESA\/SER.A\/421."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ren, P., Fang, W., and Djahel, S. (2017, January 14\u201317). A novel YOLO-Based real-time people counting approach. Proceedings of the 2017 International Smart Cities Conference (ISC2), Wuxi, China.","DOI":"10.1109\/ISC2.2017.8090864"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Baumann, D., Sommer, M., Schrempp, Y., and Sax, E. (2022, January 7\u20139). Use of Deep Learning Methods for People Counting in Public Transport. Proceedings of the 2022 International Conference on Connected Vehicle and Expo (ICCVE), Lakeland, FL, USA.","DOI":"10.1109\/ICCVE52871.2022.9742924"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1049\/ipr2.12908","article-title":"A lightweight bus passenger detection model based on YOLOv5","volume":"17","author":"Li","year":"2023","journal-title":"IET Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10846-024-02194-0","article-title":"A Passenger Detection and Action Recognition System for Public Transport Vehicles","volume":"110","author":"Lin","year":"2024","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, W., Wang, H., Qi, F., and Lu, C. (November, January 27). Explicit Shape Encoding for Real-Time Instance Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00527"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yamazaki, M., and Mori, E. (2021, January 15\u201318). Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing. Proceedings of the 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), Jodhpur, India.","DOI":"10.1109\/FG52635.2021.9666963"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhao, Q., Liu, J., Wang, Z., Liu, Y., and Yao, M. (2023, January 14\u201316). Improved YOLO-Pose Crowd Pose Estimation. Proceedings of the Proceedings of the 2023 6th International Conference on Signal Processing and Machine Learning (SPML \u201923), New York, NY, USA.","DOI":"10.1145\/3614008.3614040"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1109\/TITS.2019.2911128","article-title":"Benchmark Data and Method for Real-Time People Counting in Cluttered Scenes Using Depth Sensors","volume":"20","author":"Sun","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","first-page":"57","article-title":"People counting for public transportations using you only look once method","volume":"2","author":"Kusuma","year":"2021","journal-title":"J. Tek. Inform."},{"key":"ref_12","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.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wojke, N., and Bewley, A. (2018, January 12\u201315). Deep Cosine Metric Learning for Person Re-identification. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00087"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022, January 23\u201327). Bytetrack: Multi-object tracking by associating every detection box. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"ref_16","unstructured":"Aharon, N., Orfaig, R., and Bobrovsky, B.Z. (2022). BoT-SORT: Robust associations multi-pedestrian tracking. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.3390\/eng5040172","article-title":"A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning","volume":"5","author":"Radovan","year":"2024","journal-title":"Eng"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104195","DOI":"10.1016\/j.compind.2024.104195","article-title":"Video-based automatic people counting for public transport: On-bus versus off-bus deployment","volume":"164","author":"Mccarthy","year":"2025","journal-title":"Comput. Ind."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Konrad, J., Cokbas, M., Tezcan, M.O., and Ishwar, P. (2024). Overhead fisheye cameras for indoor monitoring: Challenges and recent progress. Front. Imaging, 3.","DOI":"10.3389\/fimag.2024.1387543"},{"key":"ref_20","unstructured":"Jocher, G. (2025, September 10). YOLOv8: State-of-the-Art Object Detection Model. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"146956","DOI":"10.1109\/ACCESS.2025.3597393","article-title":"Crowd density estimation using enhanced multi-column convolutional neural network and adaptive collation","volume":"13","author":"Serek","year":"2025","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/TCSVT.2023.3284453","article-title":"Enhancing representation learning with spatial transformation and early convolution for reinforcement learning-based small object detection","volume":"34","author":"Fang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_23","unstructured":"(2025, September 10). CVAT Documentation. Getting Started Overview. Available online: https:\/\/docs.cvat.ai\/docs\/getting_started\/overview\/."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bespalov, Y., Vysotska, O., Porvan, A., Linnyk, E., Stasenko, V., Doroshenko, G., Omiotek, Z., and Amirgaliyev, Y. (2019). Information system for recognition of biological objects in the RGB spectrum range. Information Technology in Medical Diagnostics II, CRC Press.","DOI":"10.1201\/9780429057618-13"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42454-021-00031-5","article-title":"A bi-directional adversarial explainability for decision support","volume":"3","author":"Goldberg","year":"2021","journal-title":"Hum.-Intell. Syst. Integr."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Golann, J.W., Bougher, L., Hall, R., and Espenshade, T.J. (2024). Sharing Big Video Data: Ethics, Methods, and Technology. Sociol. Methods Res.","DOI":"10.1177\/00491241241277524"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1177\/0002764218820566","article-title":"Collecting ethnographic video data for policy research","volume":"63","author":"Golann","year":"2019","journal-title":"Am. Behav. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3557","DOI":"10.1007\/s43681-025-00726-4","article-title":"Do we need watchful eyes on our workers? Ethics of using computer vision for workplace surveillance","volume":"5","author":"Sebastian","year":"2025","journal-title":"AI Ethics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s43681-023-00272-x","article-title":"The ethics of computer vision: An overview in terms of power","volume":"4","author":"Waelen","year":"2024","journal-title":"AI Ethics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1093\/idpl\/ipac001","article-title":"Complying with the GDPR when vulnerable people use smart devices","volume":"12","author":"Piasecki","year":"2022","journal-title":"Int. Data Priv. Law"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122502","DOI":"10.1016\/j.techfore.2023.122502","article-title":"Exploring ethics and human rights in artificial intelligence: A Delphi study","volume":"191","author":"Stahl","year":"2023","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_32","unstructured":"Intel Corporation (2025, September 10). Intel\u00ae RealSense\u2122 LiDAR Camera L515. Available online: https:\/\/www.intel.com\/content\/www\/us\/en\/products\/sku\/201775\/intel-realsense-lidar-camera-l515\/specifications.html."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-End Object Detection with Transformers. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_36","unstructured":"Tomasi, C., and Manduchi, R. (1998, January 7). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ryu, J., Kwak, D., and Choi, S. (2025). YOLOv8 with Post-Processing for Small Object Detection Enhancement. Appl. Sci., 15.","DOI":"10.3390\/app15137275"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pronello, C., and Garz\u00f3n Ruiz, X.R. (2023). Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study. Sensors, 23.","DOI":"10.3390\/s23187719"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Velastin, S.A., Fern\u00e1ndez, R., Espinosa, J.E., and Bay, A. (2020). Detecting, Tracking and Counting People Getting On\/Off a Metropolitan Train Using a Standard Video Camera. Sensors, 20.","DOI":"10.3390\/s20216251"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/1\/27\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T05:29:13Z","timestamp":1767763753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/1\/27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["jimaging12010027"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12010027","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]}}}