An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification
Abstract Current traffic status detection methods heavily rely on historical traffic flow data and vehicle counts. However, these methods often fail to meet the stringent real‐time requirements of state detection, especially on edge devices with limited computing resources.To address these challenge...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13279 |
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| _version_ | 1850255753889185792 |
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| author | Xin Liu Qiao Meng Xin Li Zhijie Wang Siyuan Kong Bingyu Li |
| author_facet | Xin Liu Qiao Meng Xin Li Zhijie Wang Siyuan Kong Bingyu Li |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | Abstract Current traffic status detection methods heavily rely on historical traffic flow data and vehicle counts. However, these methods often fail to meet the stringent real‐time requirements of state detection, especially on edge devices with limited computing resources.To address these challenges, this study develops a traffic alert model using temporal video frame analysis and grayscale aggregation quantization techniques. Initially, the model uses distance mapping between pixel features and frames of road traffic videos to construct a comprehensive road environment and vehicle segmentation model. The model also establishes a mapping between pixel equidistant lines and actual distances, enabling precise congestion detection. This approach significantly reduces costs associated with traditional traffic detection methods as it does not rely on historical data. Performance evaluation using fixed‐point road monitoring data indicates that the proposed model outperforms traditional traffic state detection models, with a performance improvement of approximately 4.7% to 9.5%. Additionally, the model improves computing resource efficiency by approximately 72.5% and demonstrates substantial real‐time detection capabilities. |
| format | Article |
| id | doaj-art-1a299b698cc443c4b64e9cfafbd8f0c9 |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-1a299b698cc443c4b64e9cfafbd8f0c92025-08-20T01:56:48ZengWileyIET Image Processing1751-96591751-96672024-12-0118144704471510.1049/ipr2.13279An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantificationXin Liu0Qiao Meng1Xin Li2Zhijie Wang3Siyuan Kong4Bingyu Li5School of Computer Technology and Application Qinghai University Qinghai ChinaSchool of Computer Technology and Application Qinghai University Qinghai ChinaSchool of Computer Technology and Application Qinghai University Qinghai ChinaSchool of Computer Technology and Application Qinghai University Qinghai ChinaSchool of Computer Technology and Application Qinghai University Qinghai ChinaSchool of Computer Technology and Application Qinghai University Qinghai ChinaAbstract Current traffic status detection methods heavily rely on historical traffic flow data and vehicle counts. However, these methods often fail to meet the stringent real‐time requirements of state detection, especially on edge devices with limited computing resources.To address these challenges, this study develops a traffic alert model using temporal video frame analysis and grayscale aggregation quantization techniques. Initially, the model uses distance mapping between pixel features and frames of road traffic videos to construct a comprehensive road environment and vehicle segmentation model. The model also establishes a mapping between pixel equidistant lines and actual distances, enabling precise congestion detection. This approach significantly reduces costs associated with traditional traffic detection methods as it does not rely on historical data. Performance evaluation using fixed‐point road monitoring data indicates that the proposed model outperforms traditional traffic state detection models, with a performance improvement of approximately 4.7% to 9.5%. Additionally, the model improves computing resource efficiency by approximately 72.5% and demonstrates substantial real‐time detection capabilities.https://doi.org/10.1049/ipr2.13279alarm systemsC++ languageimage processingroad trafficroad safetyroad vehicles |
| spellingShingle | Xin Liu Qiao Meng Xin Li Zhijie Wang Siyuan Kong Bingyu Li An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification IET Image Processing alarm systems C++ language image processing road traffic road safety road vehicles |
| title | An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification |
| title_full | An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification |
| title_fullStr | An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification |
| title_full_unstemmed | An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification |
| title_short | An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification |
| title_sort | innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification |
| topic | alarm systems C++ language image processing road traffic road safety road vehicles |
| url | https://doi.org/10.1049/ipr2.13279 |
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