Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections
Movement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular traje...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Future Transportation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-7590/3/2/41 |
| _version_ | 1797594714157875200 |
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| author | Udita Jana Jyoti Prakash Das Karmakar Pranamesh Chakraborty Tingting Huang Anuj Sharma |
| author_facet | Udita Jana Jyoti Prakash Das Karmakar Pranamesh Chakraborty Tingting Huang Anuj Sharma |
| author_sort | Udita Jana |
| collection | DOAJ |
| description | Movement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging, as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on the manual specification of several regions of interest. In this study, we proposed an automated classification method for movement-specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. Thereafter, a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention. |
| first_indexed | 2024-03-11T02:26:06Z |
| format | Article |
| id | doaj.art-254aafbe5cf140ffb71b57271c73fb83 |
| institution | Directory Open Access Journal |
| issn | 2673-7590 |
| language | English |
| last_indexed | 2024-03-11T02:26:06Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj.art-254aafbe5cf140ffb71b57271c73fb832023-11-18T10:31:01ZengMDPI AGFuture Transportation2673-75902023-06-013270872510.3390/futuretransp3020041Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic IntersectionsUdita Jana0Jyoti Prakash Das Karmakar1Pranamesh Chakraborty2Tingting Huang3Anuj Sharma4Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, IndiaDepartment of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, IndiaDepartment of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, IndiaETALYC Inc., Ames, IA 50010, USADepartment of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USAMovement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging, as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on the manual specification of several regions of interest. In this study, we proposed an automated classification method for movement-specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. Thereafter, a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention.https://www.mdpi.com/2673-7590/3/2/41movement classificationtrajectory analysishierarchical clustering |
| spellingShingle | Udita Jana Jyoti Prakash Das Karmakar Pranamesh Chakraborty Tingting Huang Anuj Sharma Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections Future Transportation movement classification trajectory analysis hierarchical clustering |
| title | Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections |
| title_full | Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections |
| title_fullStr | Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections |
| title_full_unstemmed | Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections |
| title_short | Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections |
| title_sort | automated approach for computer vision based vehicle movement classification at traffic intersections |
| topic | movement classification trajectory analysis hierarchical clustering |
| url | https://www.mdpi.com/2673-7590/3/2/41 |
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