A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method
High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some comp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8870211 |
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| _version_ | 1832560109960560640 |
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| author | Jooyoung Lee Jihye Byun Jaedeok Lim Jaeyun Lee |
| author_facet | Jooyoung Lee Jihye Byun Jaedeok Lim Jaeyun Lee |
| author_sort | Jooyoung Lee |
| collection | DOAJ |
| description | High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling. |
| format | Article |
| id | doaj-art-5bc220a9b1e74212a1816dd70ba0a3a8 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-5bc220a9b1e74212a1816dd70ba0a3a82025-02-03T01:28:27ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88702118870211A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling MethodJooyoung Lee0Jihye Byun1Jaedeok Lim2Jaeyun Lee3The Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, Daejeon 34051, Republic of KoreaCenter for Eco-Friendly Smart Vehicle, Korea Advanced Institute of Science and Technology, Daejeon 34051, Republic of KoreaTechnical Research Center, GnT Solution, Inc., Seoul 07255, Republic of KoreaTechnical Research Center, GnT Solution, Inc., Seoul 07255, Republic of KoreaHigh-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.http://dx.doi.org/10.1155/2020/8870211 |
| spellingShingle | Jooyoung Lee Jihye Byun Jaedeok Lim Jaeyun Lee A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method Journal of Advanced Transportation |
| title | A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method |
| title_full | A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method |
| title_fullStr | A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method |
| title_full_unstemmed | A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method |
| title_short | A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method |
| title_sort | framework for detecting vehicle occupancy based on the occupant labeling method |
| url | http://dx.doi.org/10.1155/2020/8870211 |
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