Boarding Alighting Counting in Different Transit Vehicles under Crowded Conditions
Abstract
Automatic passenger count (APC) in crowded scenes is a challenging problem because of inter-passenger movements and occlusions in public transit systems. The study proposes an effective and efficient automatic passenger counting system by innovating the tracking and counting modules, which can count passengers in crowded and occluded cases under different scenarios. The proposed algorithm is able to reduce the noise caused by passengers standing at the doors. It is applied to footage from the existing CCTV videos of the buses from Bhopal (stepped-boarding/ alighting), Delhi Metro (level-boarding/ alighting), and non-transit setup (student library). The performance of the proposed model on different setups indicates efficient detection, tracking, and counting under crowded and occluded conditions.
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Crowded boarding situations represent one of the most challenging problems in APC systems.
“Crowded boarding situations represent one of the most challenging problems in APC systems [101].”
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 [101]
Cites
Simple online and realtime tracking
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ByteTrack: Multi-object Tracking by Associating Every Detection Box
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CrowdHuman: A Benchmark for Detecting Human in a Crowd
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Estimation of the Number of Passengers in a Bus Using Deep Learning
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Passenger Counter Based on Random Forest Regressor Using Drive Recorder and Sensors in Buses
Nakashima, H., Arai, I., & Fujikawa, K. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730761 · Cited by 31 · see citing works
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Deep Learning-Based Passenger Counting System Using Surveillance Cameras
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Self-citationDeep-learning-based model for prediction of crowding in a public transit system
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Self-citationOverhead View Bus Passenger Detection and Counter using DeepSORT and Tiny-Yolo V4
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Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works
Bus Crowd Analysis Using Open CV
Mythili, N., & Janani, B. (2026) · International Conference on Artificial Intelligence & Machine Learning in Communication and Power Systems (AIMLCPS) · doi:10.1109/aimlcps68702.2026.11542438
- Authors
- Rawat, N., Jeengar, K., Agarwal, A., & Kaur Chahal, R. J.
- Affiliations
- Indian Institute of Technology Roorkee (IN)
- Indian Institute of Technology Roorkee,Mehta Family School of DS and AI,Haridwar,India
- Venue
- 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)
- Publisher
- IEEE
- Published
- 2024-07-12
- Pages
- 1-6
- OpenAlex
- W4402673905
- Cited by
- 2
- References
- 10
- Citations by year
- 2026: 1 · 2024: 1
- FWCI
- 0.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (publishedVersion)
- 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
- automatic passenger counting, boarding and alighting, crowded scenes, occlusion handling, human tracking, CCTV surveillance, public transport, bus boarding, subway stations
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