Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm
As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weath...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9373414/ |
| _version_ | 1819180824437719040 |
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| author | Seungyun Jeong Seungbin Roh Keemin Sohn |
| author_facet | Seungyun Jeong Seungbin Roh Keemin Sohn |
| author_sort | Seungyun Jeong |
| collection | DOAJ |
| description | As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data. |
| first_indexed | 2024-12-22T22:20:29Z |
| format | Article |
| id | doaj.art-92b8ac56cb3c487dbadb2e9ea8550855 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T22:20:29Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-92b8ac56cb3c487dbadb2e9ea85508552022-12-21T18:10:40ZengIEEEIEEE Access2169-35362021-01-019409804099510.1109/ACCESS.2021.30646039373414Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection AlgorithmSeungyun Jeong0https://orcid.org/0000-0002-6276-620XSeungbin Roh1https://orcid.org/0000-0001-7705-0282Keemin Sohn2https://orcid.org/0000-0002-7270-7094Department of Urban Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Urban Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Urban Engineering, Chung-Ang University, Seoul, South KoreaAs a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data.https://ieeexplore.ieee.org/document/9373414/Change-point algorithmautoencodertraffic surveillancemulti-regime modeltraffic density |
| spellingShingle | Seungyun Jeong Seungbin Roh Keemin Sohn Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm IEEE Access Change-point algorithm autoencoder traffic surveillance multi-regime model traffic density |
| title | Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm |
| title_full | Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm |
| title_fullStr | Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm |
| title_full_unstemmed | Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm |
| title_short | Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm |
| title_sort | multi regime analysis for computer vision based traffic surveillance using a change point detection algorithm |
| topic | Change-point algorithm autoencoder traffic surveillance multi-regime model traffic density |
| url | https://ieeexplore.ieee.org/document/9373414/ |
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