Passenger flow counting in buses based on deep learning using surveillance video
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Using multiple cameras and image datasets that cover different scenarios like day, night, rain, etc. has a significant effect on achieving better results in passenger detection in public transport.
“Using multiple cameras and image datasets that cover different scenarios like day, night, rain, etc. proved to have a significant effect on achieving better results in passenger detection in public transport.”
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 [38]
The detection of open doors improves precision, so the counting process should start when the doors are open and not change the number of passengers while driving.
“Research like [38] proved that the detection of open doors improves precision, so the counting process starts when the doors are open, and it does change the number of passengers while driving.”
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 [38]
ML techniques and models can interpret complex patterns, identify individual passengers in various settings, and adapt to changing conditions.
“ML techniques and models can interpret complex patterns, identify individual passengers in various settings, and adapt to changing conditions [38].”
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 [38]
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- Authors
- Hsu, Y.-W., Wang, T.-Y., & Perng, J.-W.
- Affiliations
- National Sun Yat-sen University (TW)
- Corresponding author
- Perng, Jau-Woei
- Venue
- Optik
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 202
- Pages
- 163675
- ISSN
- 0030-4026 (ISSN-L)
- OpenAlex
- W2985210540
- Cited by
- 43
- References
- 59
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 9 · 2023: 7 · 2022: 10 · 2021: 7 · 2020: 2
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Optik (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan
- 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 › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- surveillance video, Single Shot MultiBox Detector, particle filter, data association, passenger tracking, door opening detection, occlusion handling, dense crowds, public transport
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