Clustering method for counting passengers getting in a bus with single camera
Abstract
Automatic counting of passengers is very important for both business and security applications. We present a single-camera-based vision system that is able to count passengers in a highly crowded situation at the entrance of a traffic bus. The unique characteristics of the proposed system include, First, a novel feature-point-tracking- and online clustering-based passenger counting framework, which performs much better than those of background-modeling-and foreground-blob-tracking-based methods. Second, a simple and highly accurate clustering algorithm is developed that projects the high-dimensional feature point trajectories into a 2-D feature space by their appearance and disappearance times and counts the number of people through online clustering. Finally, all test video sequences in the experiment are captured from a real traffic bus in Shanghai, China. The results show that the system can process two 320×240 video sequences at a frame rate of 25 fps simultaneously, and can count passengers reliably in various difficult scenarios with complex interaction and occlusion among people. The method achieves high accuracy rates up to 96.5%.
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A single camera bus system based on feature-point tracking and online clustering can count passengers in heavy occlusion and crowding.
“The counting system was based on feature-point tracking and an online clustering framework to count passengers in complex situations with heavy occlusion and crowding.”
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 [23]
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- Authors
- Yang, T., Zhang, Y., Shao, D., & Li, Y.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Yang, Tao
- ORCID
- Yang, Tao
- Venue
- Optical Engineering
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2010-03-01
- Volume
- 49
- Issue
- 03
- Pages
- 1-10
- ISSN
- 0091-3286 (ISSN-L)
- 1560-2303
- OpenAlex
- W2034430585
- Cited by
- 27
- References
- 20
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 1 · 2015: 1 · 2014: 2 · 2013: 2 · 2012: 2
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Optical Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- passenger counting, online clustering, feature point tracking, crowded scenes, occlusion handling, real-time video processing
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