REAL-TIME VIDEO-BASED VISITOR COUNTING FOR SMART TOURISM DESTINATIONS USING YOLOV11 AND BYTETRACK
DOI:
https://doi.org/10.33480/jitk.v11i4.7759Keywords:
ByteTrack, Multi-Object Tracking, Smart Tourism, Visitor Counting, YOLOv11Abstract
Accurate and real-time visitor data are needed to support smart tourism management. However, conventional counting methods still have limitations in dynamic outdoor tourism environments. This study develops and evaluates a real-time video-based visitor counting system by integrating YOLOv11 for person detection and ByteTrack for multi-object tracking. This approach extends visitor counting evaluation to uncontrolled open-air tourist destinations, where lighting variation, background complexity, visitor movement, and crowd density may affect detection and tracking performance. The system was evaluated using nine Full HD videos from five tourist destinations in West Sumatra, recorded under daylight and afternoon conditions with low to medium visitor densities. The YOLOv11–ByteTrack system achieved an average counting accuracy of 84.02%, MAE of 7.22 visitors per video, MAPE of 15.98%, and an average processing speed of 36.23 FPS. The average accuracy exceeded those of YOLOv3 and YOLOv8, which achieved 75.71% and 77.15%, respectively. These findings suggest that YOLOv11–ByteTrack has practical potential as a real-time visitor counting approach in smart tourism management, particularly for monitoring visitor flows, assessing site capacity, controlling visitor density, and supporting data-driven infrastructure planning.
Downloads
References
[1] I. Sustacha, J. F. Baños-Pino, and E. del Valle, “The role of technology in enhancing the tourism experience in smart destinations: A meta-analysis,” Journal of Destination Marketing & Management, vol. 30, p. 100817, 2023, doi: 10.1016/j.jdmm.2023.100817.
[2] S. Bingöl and Y. Yang, “Integrating smart technologies and artificial intelligence to build smart tourism destination ecosystems: A model for smart destination management,” Tourism Management Perspectives, vol. 58, p. 101380, 2025, doi: 10.1016/j.tmp.2025.101380.
[3] M. A. Celdrán-Bernabéu, J.-N. Mazón, D. Giner-Sánchez, J. Morales-García, and M. P. Peñarrubia-Zaragoza, “Smart tourism destinations as open data providers: Barriers and opportunities,” Journal of Destination Marketing & Management, vol. 40, p. 101065, 2026, doi: 10.1016/j.jdmm.2025.101065.
[4] C. N. Novera, Z. Ahmed, R. Kushol, P. Wanke, and Md. A. K. Azad, “Internet of Things (IoT) in smart tourism: A literature review,” Spanish Journal of Marketing - ESIC, vol. 26, no. 3, pp. 325–344, 2022, doi: 10.1108/SJME-03-2022-0035.
[5] J.-W. Bi, C. Li, H. Xu, and H. Li, “Forecasting daily tourism demand for tourist attractions with big data: An ensemble deep learning method,” Journal of Travel Research, vol. 61, no. 8, pp. 1719–1737, 2022, doi: 10.1177/00472875211040569.
[6] M. Mariani and R. Baggio, “Big data and analytics in hospitality and tourism: A systematic literature review,” International Journal of Contemporary Hospitality Management, vol. 34, no. 1, pp. 231–278, 2022, doi: 10.1108/IJCHM-03-2021-0301.
[7] Y. Cai, G. Li, L. Wen, and C. Liu, “Intellectual landscape and emerging trends of big data research in hospitality and tourism: A scientometric analysis,” International Journal of Hospitality Management, vol. 117, p. 103633, 2024, doi: 10.1016/j.ijhm.2023.103633.
[8] S. Park, “Big data in smart tourism: A perspective article,” Journal of Smart Tourism, vol. 1, no. 3, pp. 3–5, 2021, doi: 10.52255/smarttourism.2021.1.3.2.
[9] D. P. Sakas, D. P. Reklitis, M. C. Terzi, and C. Vassilakis, “Multichannel digital marketing optimizations through big data analytics in the tourism and hospitality industry,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 17, no. 4, pp. 1383–1408, 2022, doi: 10.3390/jtaer17040070.
[10] J. B. Read, M. Daniels, and L. Harmon, “Implementing technology-based visitor counts in parks: A methodological overview,” Journal of Park and Recreation Administration, vol. 39, no. 1, pp. 85–103, 2021, doi: 10.18666/JPRA-2020-10502.
[11] M. J. Daniels, H.-L. Liu, and S. L. Powers, “Infrared visitor counts: Data validation and algorithm development,” Current Issues in Tourism, vol. 28, no. 14, pp. 2215–2219, 2025, doi: 10.1080/13683500.2024.2364758.
[12] D. Schmücker et al., “The INPReS intervention escalation framework for avoiding overcrowding in tourism destinations,” Tourism and Hospitality, vol. 4, no. 2, pp. 282–292, 2023, doi: 10.3390/tourhosp4020017.
[13] J. Reif, D. Schmücker, L. Naschert, and E. Horster, “Visitor management in tourism destinations: Current challenges in measuring and managing visitors’ spatio-temporal behaviour,” in Tourism Destination Development: A Geographic Perspective on Destination Management and Tourist Demand, M. Pillmayer, M. Karl, and M. Hansen, Eds. Berlin, Germany: De Gruyter, 2024, pp. 81–104, doi: 10.1515/9783110794090-005.
[14] S. Zhang and A. Chen, “Do different queue formations influence the overestimation of tourism carrying capacity?,” Sustainability, vol. 16, no. 24, p. 11047, 2024, doi: 10.3390/su162411047.
[15] G. Lupp et al., “Visitor counting and monitoring in forests using camera traps: A case study from bavaria (Southern Germany),” Land, vol. 10, no. 7, p. 736, 2021, doi: 10.3390/land10070736.
[16] J. Staab, E. Udas, M. Mayer, H. Taubenböck, and H. Job, “Comparing established visitor monitoring approaches with triggered trail camera images and machine learning based computer vision,” Journal of Outdoor Recreation and Tourism, vol. 35, p. 100387, 2021, doi: 10.1016/j.jort.2021.100387.
[17] A. Panigrahy and A. Verma, “Tourist experiences: A systematic literature review of computer vision technologies in smart destination visits,” Journal of Tourism Futures, vol. 11, no. 2, pp. 187–202, 2025, doi: 10.1108/JTF-04-2024-0073.
[18] X. Wang, “Construction of smart tourism system integrating tourist needs and scene characteristics,” Systems and Soft Computing, vol. 6, p. 200168, 2024, doi: 10.1016/j.sasc.2024.200168.
[19] D. Nurseitov, K. Bostanbekov, N. Toiganbayeva, A. Zhalgas, D. Yedilkhan, and B. Amirgaliyev, “Vision-based people counting and tracking for urban environments,” Journal of Imaging, vol. 12, no. 1, p. 27, 2026, doi: 10.3390/jimaging12010027.
[20] N. Krishnachaithanya et al., “People counting in public spaces using deep learning-based object detection and tracking techniques,” in 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India, 2023, pp. 784–788, doi: 10.1109/CISES58720.2023.10183503.
[21] K. Wijayanti, G. A. Mutiara, B. Suryawardani, E. Ervina, and G. P. Kusuma, “Non-intrusive real-time tourist crowd monitoring for overtourism mitigation using YOLOv8-based head detection and tracking,” Journal of Robotics and Control (JRC), vol. 6, no. 4, pp. 1985–2004, 2025, doi: 10.18196/jrc.v6i4.26396.
[22] M. Ş. Gündüz and G. Işık, “A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models,” Journal of Real-Time Image Processing, vol. 20, no. 1, p. 5, 2023, doi: 10.1007/s11554-023-01276-w.
[23] W. Zhang, J. Calautit, P. W. Tien, Y. Wu, and S. Wei, “Deep learning models for vision-based occupancy detection in high occupancy buildings,” Journal of Building Engineering, vol. 98, p. 111355, 2024, doi: 10.1016/j.jobe.2024.111355.
[24] A. Jierula, S. Wang, T.-M. Oh, and P. Wang, “Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data,” Applied Sciences, vol. 11, no. 5, p. 2314, 2021, doi: 10.3390/app11052314.
[25] H. Khoshvaght, R. R. Permala, A. Razmjou, and M. Khiadani, “A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction,” Journal of Environmental Chemical Engineering, vol. 13, no. 6, p. 119675, 2025, doi: 10.1016/j.jece.2025.119675.
[26] R. Khanam and M. Hussain, “YOLOv11: An overview of the key architectural enhancements,” arXiv preprint, arXiv:2410.17725, 2024, doi: 10.48550/arXiv.2410.17725.
[27] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023, pp. 7464–7475, doi: 10.1109/CVPR52729.2023.00721.
[28] Y. Zhang et al., “ByteTrack: Multi-object tracking by associating every detection box,” in Computer Vision – ECCV 2022, Cham: Springer Nature Switzerland, 2022, pp. 1–21, doi: 10.1007/978-3-031-20047-2_1.
[29] L. Deng, Q. Zhou, S. Wang, J. M. Górriz, and Y. Zhang, “Deep learning in crowd counting: A survey,” CAAI Transactions on Intelligence Technology, vol. 9, no. 5, pp. 1043–1077, 2024, doi: 10.1049/cit2.12241.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Feriantano Sundang Pranata, Arif Adrian, Khairani Saladin

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






-a.jpg)
-b.jpg)











