Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization
Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1456 |
| _version_ | 1797263860714962944 |
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| author | Shenglin Li Hwan-Sik Yoon |
| author_facet | Shenglin Li Hwan-Sik Yoon |
| author_sort | Shenglin Li |
| collection | DOAJ |
| description | Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO. |
| first_indexed | 2024-04-25T00:19:43Z |
| format | Article |
| id | doaj.art-1f4b127cacd34cdbb3710b9c45025284 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:19:43Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1f4b127cacd34cdbb3710b9c450252842024-03-12T16:54:50ZengMDPI AGSensors1424-82202024-02-01245145610.3390/s24051456Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm OptimizationShenglin Li0Hwan-Sik Yoon1Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USADepartment of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USARecent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.https://www.mdpi.com/1424-8220/24/5/1456camera calibrationtraffic surveillancegenetic algorithmparticle swarm optimization |
| spellingShingle | Shenglin Li Hwan-Sik Yoon Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization Sensors camera calibration traffic surveillance genetic algorithm particle swarm optimization |
| title | Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization |
| title_full | Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization |
| title_fullStr | Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization |
| title_full_unstemmed | Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization |
| title_short | Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization |
| title_sort | enhancing camera calibration for traffic surveillance with an integrated approach of genetic algorithm and particle swarm optimization |
| topic | camera calibration traffic surveillance genetic algorithm particle swarm optimization |
| url | https://www.mdpi.com/1424-8220/24/5/1456 |
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