Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing
Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-02-01
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| Series: | Journal of Traffic and Transportation Engineering (English ed. Online) |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095756424000072 |
| _version_ | 1797300389934006272 |
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| author | Frank Ngeni Judith Mwakalonge Saidi Siuhi |
| author_facet | Frank Ngeni Judith Mwakalonge Saidi Siuhi |
| author_sort | Frank Ngeni |
| collection | DOAJ |
| description | Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration. |
| first_indexed | 2024-03-07T23:06:28Z |
| format | Article |
| id | doaj.art-d0c5cd4dcbf543d7859aa045b280a7e5 |
| institution | Directory Open Access Journal |
| issn | 2095-7564 |
| language | English |
| last_indexed | 2024-03-07T23:06:28Z |
| publishDate | 2024-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Traffic and Transportation Engineering (English ed. Online) |
| spelling | doaj.art-d0c5cd4dcbf543d7859aa045b280a7e52024-02-22T04:52:02ZengKeAi Communications Co., Ltd.Journal of Traffic and Transportation Engineering (English ed. Online)2095-75642024-02-01111115Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computingFrank Ngeni0Judith Mwakalonge1Saidi Siuhi2Corresponding author.; Department of Engineering, South Carolina State University, Orangeburg, SC 29117, USADepartment of Engineering, South Carolina State University, Orangeburg, SC 29117, USADepartment of Engineering, South Carolina State University, Orangeburg, SC 29117, USAInaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.http://www.sciencedirect.com/science/article/pii/S2095756424000072Traffic classificationTraffic countingDeepSORTYOLOv5Quantum computing |
| spellingShingle | Frank Ngeni Judith Mwakalonge Saidi Siuhi Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing Journal of Traffic and Transportation Engineering (English ed. Online) Traffic classification Traffic counting DeepSORT YOLOv5 Quantum computing |
| title | Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing |
| title_full | Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing |
| title_fullStr | Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing |
| title_full_unstemmed | Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing |
| title_short | Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing |
| title_sort | solving traffic data occlusion problems in computer vision algorithms using deepsort and quantum computing |
| topic | Traffic classification Traffic counting DeepSORT YOLOv5 Quantum computing |
| url | http://www.sciencedirect.com/science/article/pii/S2095756424000072 |
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