Video-based automatic people counting for public transport: On-bus versus off-bus deployment
Open accessAbstract
Interest in Automatic People Counting (APC) for crowd detection and management is rapidly growing. While a range of Internet of Things (IoT) sensors and systems exist, video analytics is emerging as a particularly attractive option — especially for applications where more traditional methods of people counting are not available, unreliable or expensive. In this paper we focus on automatic people counting in the public transport context – specifically, rail replacement bus services – in which bus companies are typically contracted to provide bus services to replace train services during periods of planned and unplanned line disruption. This presents a particularly compelling use-case for video-based people counting, while also presenting unique challenges. Field trials are thus vital to the proper assessment of video-based APC solutions, however remain relatively scarce in the literature. While datasets to support research and benchmarking exist, these do not capture the intrinsic complexities of real-world deployment and the implications of selected configurations — in particular, on-vehicle versus off-vehicle use cases. In this paper, we evaluate our own video-based APC solution, representative of state-of-the-art approaches in the literature, in two separate extensive (i.e, multi-day) metropolitan field trials covering both on and off-bus use-cases. Through real-world deployment of the system in both settings, we highlight key differences with respect to APC accuracy, as well as other practical considerations, and the validity of underlying assumptions in both on and off-bus scenarios. • A practical evaluation of video-based passenger counting in real-world use-cases. • Field trials comparing on-bus versus off-bus deployment configurations • Insights on configuration, environmental factors, and operational considerations. • Priorities for future research on scalable, low-cost real-time video-based APC systems.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Public transport vehicles operate under varying conditions such as different lighting, weather, and passenger densities, all of which can affect the performance of image processing algorithms.
“Public transport vehicles operate under varying conditions such as different lighting, weather, and passenger densities, all of which can affect the performance of image processing algorithms [90].”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [90]
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307 · see citing works
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., & Paulus, D. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296962 · Cited by 5,286 · see citing works
2011 International Conference on Computer Vision
(2011) · doi:10.1109/iccv17851.2011 · Cited by 2,895 · see citing works
StrongSORT: Make DeepSORT Great Again
Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., & Meng, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3240881 · Cited by 1,191 · see citing works
A survey of recent advances in CNN-based single image crowd counting and density estimation
Sindagi, V. A., & Patel, V. M. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.07.007 · Cited by 634 · see citing works
2007 IEEE International Conference on Image Processing
SABRI, A., Boonært, J., Lecoeuche, S., & MOUADDIB, E. M. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379496 · Cited by 547 · see citing works
IJCAI-83: International joint conference on artificial intelligence
(1982) · Artificial Intelligence · doi:10.1016/0004-3702(82)90046-7 · Cited by 317 · see citing works
2014 IEEE Intelligent Vehicles Symposium Proceedings
(2014) · doi:10.1109/iv32400.2014 · Cited by 300 · see citing works
Crowd Counting using Deep Recurrent Spatial-Aware Network
Liu, L., Wang, H., Li, G., Ouyang, W., & Lin, L. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/118 · Cited by 201 · see citing works
2007 IEEE Intelligent Vehicles Symposium
(2007) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2007.4290059 · Cited by 164 · see citing works
CNN-based Density Estimation and Crowd Counting: A SurveyPreprint
Gao, G., Gao, J., Liu, Q., Wang, Q., & Wang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12783 · Cited by 142 · see citing works
Real-Time Human Detection and Counting System Using Deep Learning Computer Vision Techniques
Mokayed, H., Quan, T. Z., Alkhaled, L., & Sivakumar, V. (2022) · Artificial Intelligence and Applications · doi:10.47852/bonviewaia2202391 · Cited by 135 · see citing works
Locality-Constrained Spatial Transformer Network for Video Crowd Counting
Fang, Y., Zhan, B., Cai, W., Gao, S., & Hu, B. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00145 · Cited by 130 · see citing works
Applications of deep learning in congestion detection, prediction and alleviation: A survey
Kumar, N., & Raubal, M. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103432 · Cited by 124 · see citing works
PaDNet: Pan-Density Crowd Counting
Tian, Y., Lei, Y., Zhang, J., & Wang, J. Z. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2952083 · Cited by 119 · see citing works
A Distributed PIR-based Approach for Estimating People Count in Office Environments
Wahl, F., Milenkovic, M., & Amft, O. (2012) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/iccse.2012.92 · Cited by 113 · see citing works
2010 International Conference on System Science and Engineering
(2010) · doi:10.1109/icsse16288.2010 · Cited by 101 · see citing works
Passenger flow estimation based on convolutional neural network in public transportation system
Liu, G., Yin, Z., Jia, Y., & Xie, Y. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.02.016 · Cited by 86 · see citing works
CeilingSee: Device-free occupancy inference through lighting infrastructure based LED sensing
Yang, Y., Hao, J., Luo, J., & Pan, S. J. (2017) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2017.7917871 · Cited by 82 · see citing works
Passenger flow prediction in bus transportation system using deep learning
Nagaraj, N., Gururaj, H. L., Swathi, B. H., & Hu, Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12306-3 · Cited by 70 · see citing works
Pa-Count: Passenger Counting in Vehicles Using Wi-Fi Signals
Jiang, H., Chen, S., Xiao, Z. Q., Hu, J., Liu, J., & Dustdar, S. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3263229 · Cited by 70 · see citing works
Velastín, S. A., Fernández, R., Espinosa, J. E., & Bay, A. (2020) · Sensors · doi:10.3390/s20216251 · Cited by 62 · see citing works
Ji, Y., Mishalani, R. G., & McCord, M. R. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.04.021 · Cited by 61 · see citing works
Survey of Non-Image-Based Approaches for Counting People
Kouyoumdjieva, S. T., Danielis, P., & Karlsson, G. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2902824 · Cited by 61 · see citing works
Counting public transport passenger using WiFi signatures of mobile devices
Myrvoll, T. A., Hakegard, J. E., Matsui, T., & Septier, F. (2017) · 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317687 · Cited by 60 · see citing works
LCDnet: a lightweight crowd density estimation model for real-time video surveillance
Khan, M. A., Menouar, H., & Hamila, R. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01286-8 · Cited by 51 · see citing works
Gawande, U. H., Hajari, K. O., & Golhar, Y. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.90810 · Cited by 48 · see citing works
Passenger flow counting in buses based on deep learning using surveillance video
Hsu, Y.-W., Wang, T.-Y., & Perng, J.-W. (2020) · Optik · doi:10.1016/j.ijleo.2019.163675 · Cited by 43 · see citing works
Gandhi, T., & Trivedi, M. M. (2007) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2007.4290258 · Cited by 38 · see citing works
Estimation of left behind subway passengers through archived data and video image processing
Sipetas, C., Keklikoglou, A., & Gonzales, E. J. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102727 · Cited by 37 · see citing works
Lesani, A., Nateghinia, E., & Miranda-Moreno, L. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.01.018 · Cited by 36 · see citing works
The design and implementation of a vision-based people counting system in buses
Perng, J., Wang, T.-Y., Hsu, Y.-W., & Wu, B. (2016) · International Conference on System Science and Engineering · doi:10.1109/icsse.2016.7551620 · Cited by 35 · see citing works
A Field Study of Internet of Things-Based Solutions for Automatic Passenger Counting
McCarthy, C. D., Moser, I., Jayaraman, P. P., Ghaderi, ., Tan, A. M., Yavari, A., Mehmood, U., Simmons, M. D., Weizman, Y., Georgakopoulos, D., et al. (2021) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2021.3111052 · Cited by 34 · see citing works
Self-citationPassenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges
Kuchár, P., Pirník, R., Janota, A., Malobický, B., Kubík, J., & Šišmišová, D. (2023) · Sustainability · doi:10.3390/su15021332 · Cited by 30 · see citing works
Detection of passenger flow on and off buses based on video images and YOLO algorithm
Zhao, J., Li, C., Xu, Z., Jiao, L., Zhao, Z., & Wang, Z. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10747-w · Cited by 28 · see citing works
Zhang, X., Sun, Y., Guan, F., Chen, K., Witlox, F., & Huang, H. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103854 · Cited by 25 · see citing works
Hybrid model for predicting anomalous large passenger flow in urban metros
Zheng, Z., Ling, X., Wang, P., Xiao, J., & Zhang, F. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2020.0054 · Cited by 24 · see citing works
Xie, C., Daghero, F., Chen, Y., Castellano, M., Gandolfi, L., Calimera, A., Macii, E., Poncino, M., & Pagliari, D. J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263290 · Cited by 22 · see citing works
CCTV Video Analytics: Recent Advances and Limitations
Velastín, S. A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-05036-7_3 · Cited by 19 · see citing works
Ghaziamin, P., Bajaj, K., Bouguila, N., & Patterson, Z. (2024) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc59802.2024.00011 · Cited by 17 · see citing works
Pedestrian Detection and Tracking for Counting Applications in Metro Station
Yan-yan, C., Chen, N., Yu-yang, Z., Wu, K.-H., & Wei-wei, Z. (2014) · Discrete Dynamics in Nature and Society · doi:10.1155/2014/712041 · Cited by 14 · see citing works
Zheng, Z., Liu, W., Wang, H., Fan, G., & Dai, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3054938 · Cited by 13 · see citing works
Haq, E. U., Huarong, X., Xuhui, C., Wanqing, Z., Jianping, F., & Abid, F. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08167-y · Cited by 12 · see citing works
Dahlan, I. A., Putra, M. B. G., Supangkat, S. H., Hidayat, F., Lubis, F. F., & Hamami, F. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i2.pp773-784 · Cited by 9 · see citing works
Artificial Intelligence and Applications
(2024) · doi:10.5121/csit.2024.1401 · Cited by 8 · see citing works
Appearance-based passenger counting in cluttered scenes with lateral movement compensation
Sutopo, R., Lim, J. M.-Y., Baskaran, V. M., Wong, K., Massimo Tistarelli, A., & Liau, H. F. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05760-x · Cited by 7 · see citing works
Fast and Accurate Person Re-Identification with RMNetPreprint
Izutov, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02465 · Cited by 7 · see citing works
Automatic Passenger Counting on the Edge via Unsupervised Clustering
Delzanno, G., Caputo, L., D’Agostino, D., Grosso, D., Mustajab, A. H., Bixio, L., & Rulli, M. (2023) · Sensors · doi:10.3390/s23115210 · Cited by 7 · see citing works
Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras
Komatsu, S., Furuta, R., & Taniguchi, Y. (2021) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr51284.2021.00038 · Cited by 5 · see citing works
Zaman, H. B. (2009) · Springer eBooks · Cited by 5 · see citing works
Video-based automatic transit vehicle ingress/egress counting using trajectory clustering
Cheng, G., Huang, Y., Mirzaei, A., Buckles, B. P., & Yang, H. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856514 · Cited by 4 · see citing works
Passenger Location Estimation in Public Transport: Evaluating Methods and Camera Placement Impact
Marczyk, M., Kempski, A., Socha, M., Cogiel, M., Foszner, P., & Staniszewski, M. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3434746 · Cited by 4 · see citing works
Tian, Y., Sun, T., Wang, X., & Gong, Y. (2021) · International Conference on Intelligent Computing, Automation and Systems (ICICAS) · doi:10.1109/icicas53977.2021.00074 · Cited by 1 · see citing works
(2021) · HAL
Flagged first, then by citations
2 of the 62 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works
Vision-Based People Counting and Tracking for Urban Environments
Nurseitov, D. B., Bostanbekov, K. A., Toiganbayeva, N., Zhalgas, A., Yedilkhan, D., & Amirgaliyev, B. (2026) · Journal of Imaging · doi:10.3390/jimaging12010027 · Cited by 5 · see citing works
Development of a Depth Imaging-Based Passenger Counting Sensor for Public Transportation
Thandassery, S., Zhang, Y., Li, B., Saada, M., Cai, H., Meng, Q., Zhu, Q., & Han, Q. (2025) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics66191.2025.11332612 · Cited by 1 · see citing works
Vahan Samvedak: a comprehensive real-time college bus tracking and safety system
Chauhan, Y., Dhiman, S., Negi, S., Bamola, J., Pandey, K., Tripathi, N., Goyal, S., Gupta, S., & Kumar, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-48862-z · Cited by 1 · see citing works
Wi-Fi Probe Data-Calibrated Simulation Modeling of Passenger Flow in Urban Rail Transit Stations
Mo, Y., Cao, W., Li, W., Liu, P., & Luo, Q. (2026) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-9425
Niu, Z., Xiao, G., Pi, H., Yang, S., & Tang, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3688860
A dynamic model for optimising fare inspector itineraries in public transport networks
Yin, T., Nassir, N., Ghaderi, H., & Tanin, E. (2026) · Transportation · doi:10.1007/s11116-026-10766-2
Self-citationMultiCrowd: Crowd Management for Smart City for Concurrent Requests
Chakraborty, S., & Majumder, A. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-18470-2_33
Privacy-by-Architecture for Passenger Counting in Growing Economies
Kyatha, N. (2026) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf68042.2026.11558145
SLA Compliance in TOSCA-Based Multi-Access Edge Computing Deployments
Santos, Á., Bernardino, J., & Correia, N. (2026) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2026.3714735
Duc, K. N., Nhat, L. C., Minh, P. H. D., Thoai, G.-T. T.-L., Duy, M.-D. T.-N., Hai, T. H. L., & Thoai, N. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2888-1_5
People Counting Using YOLO-Based Detection and Clustering for a Mobile Robot
Gomulka, K., Woźniak, P., & Krzeszowski, T. (2026) · Sensors · doi:10.3390/s26154664
Artificial intelligence in urban rail transit operations: A review
Yang, Z., Chen, F., Yang, L., Ma, W., & Zhang, J. (2026) · Artificial Intelligence for Transportation · doi:10.1016/j.ait.2026.100078
Local Spacing-Aware Hungarian Matching for Stable Point-Supervised Crowd Counting
Jiang, K., Lin, Y., & Ao, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37138-6_7
- Authors
- McCarthy, C., Ghaderi, H., Martí, F., Jayaraman, P., & Dia, H.
- Affiliations
- Swinburne University of Technology (AU)
- Corresponding author
- McCarthy, Chris
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2025-01
- Volume
- 164
- Pages
- 104195
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W4402889922
- Cited by
- 14
- References
- 62
- Citations by year
- 2026: 12 · 2025: 1 · 2024: 1
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers in Industry (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Ocean Engineering › Evacuation and Crowd Dynamics
- Keywords
- video analysis, public transport, bus services, crowd detection, field trials, real-time video processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
