Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis
Vision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-10-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2012.0185 |
| _version_ | 1797685220210638848 |
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| author | Mau‐Tsuen Yang Rang‐Kai Jhang Jia‐Sheng Hou |
| author_facet | Mau‐Tsuen Yang Rang‐Kai Jhang Jia‐Sheng Hou |
| author_sort | Mau‐Tsuen Yang |
| collection | DOAJ |
| description | Vision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can reliably perform traffic flow estimation and vehicle classification at the same time. First, the foreground is extracted using a pixel‐wise weighting list that models the dynamic background. Shadows are discriminated utilising colour and edge invariants. Second, the foreground on a specified check‐line is then collected over time to form a spatial–temporal profile image. Third, the traffic flow is estimated by counting the number of connected components in the profile image. Finally, the vehicle type is classified according to the size of the foreground mask region. In addition, several traffic measures, including traffic velocity, flow, occupancy and density, are estimated based on the analysis of the segmentation. The availability and reliability of these traffic measures provides critical information for public transportation monitoring and intelligent traffic control. Since the proposed method only process a small area close to the check‐line to collect the spatial–temporal profile for analysis, the complete system is much more efficient than existing visual traffic flow estimation methods. |
| first_indexed | 2024-03-12T00:41:00Z |
| format | Article |
| id | doaj.art-2c8d7fa6366c48f7b28cb190da72a5bc |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:41:00Z |
| publishDate | 2013-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-2c8d7fa6366c48f7b28cb190da72a5bc2023-09-15T07:13:27ZengWileyIET Computer Vision1751-96321751-96402013-10-017539440410.1049/iet-cvi.2012.0185Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysisMau‐Tsuen Yang0Rang‐Kai Jhang1Jia‐Sheng Hou2Department of Computer Science and Information EngineeringNational Dong‐Hwa UniversityTaiwanDepartment of Computer Science and Information EngineeringNational Dong‐Hwa UniversityTaiwanDepartment of Computer Science and Information EngineeringNational Dong‐Hwa UniversityTaiwanVision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can reliably perform traffic flow estimation and vehicle classification at the same time. First, the foreground is extracted using a pixel‐wise weighting list that models the dynamic background. Shadows are discriminated utilising colour and edge invariants. Second, the foreground on a specified check‐line is then collected over time to form a spatial–temporal profile image. Third, the traffic flow is estimated by counting the number of connected components in the profile image. Finally, the vehicle type is classified according to the size of the foreground mask region. In addition, several traffic measures, including traffic velocity, flow, occupancy and density, are estimated based on the analysis of the segmentation. The availability and reliability of these traffic measures provides critical information for public transportation monitoring and intelligent traffic control. Since the proposed method only process a small area close to the check‐line to collect the spatial–temporal profile for analysis, the complete system is much more efficient than existing visual traffic flow estimation methods.https://doi.org/10.1049/iet-cvi.2012.0185traffic flow estimationvehicle-type classificationvision-based spatial–temporal profile analysisvision-based traffic surveillancetraffic managementoutdoor illumination |
| spellingShingle | Mau‐Tsuen Yang Rang‐Kai Jhang Jia‐Sheng Hou Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis IET Computer Vision traffic flow estimation vehicle-type classification vision-based spatial–temporal profile analysis vision-based traffic surveillance traffic management outdoor illumination |
| title | Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis |
| title_full | Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis |
| title_fullStr | Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis |
| title_full_unstemmed | Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis |
| title_short | Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis |
| title_sort | traffic flow estimation and vehicle type classification using vision based spatial temporal profile analysis |
| topic | traffic flow estimation vehicle-type classification vision-based spatial–temporal profile analysis vision-based traffic surveillance traffic management outdoor illumination |
| url | https://doi.org/10.1049/iet-cvi.2012.0185 |
| work_keys_str_mv | AT mautsuenyang trafficflowestimationandvehicletypeclassificationusingvisionbasedspatialtemporalprofileanalysis AT rangkaijhang trafficflowestimationandvehicletypeclassificationusingvisionbasedspatialtemporalprofileanalysis AT jiashenghou trafficflowestimationandvehicletypeclassificationusingvisionbasedspatialtemporalprofileanalysis |