Assembling three one-camera images for three-camera intersection classification
Determining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, l...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2023-10-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2023-0100 |
| _version_ | 1797633931102650368 |
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| author | Marcella Astrid Seung-Ik Lee |
| author_facet | Marcella Astrid Seung-Ik Lee |
| author_sort | Marcella Astrid |
| collection | DOAJ |
| description | Determining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three-camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian-view intersection classification experiments show that our feature fusion model provides an area under the curve and F1-score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three- and one-camera models. |
| first_indexed | 2024-03-11T12:01:36Z |
| format | Article |
| id | doaj.art-4d9345e38e0446a6bcf1fa6b50c34ead |
| institution | Directory Open Access Journal |
| issn | 1225-6463 |
| language | English |
| last_indexed | 2024-03-11T12:01:36Z |
| publishDate | 2023-10-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-4d9345e38e0446a6bcf1fa6b50c34ead2023-11-08T05:10:22ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632023-10-0145586287310.4218/etrij.2023-010010.4218/etrij.2023-0100Assembling three one-camera images for three-camera intersection classificationMarcella AstridSeung-Ik LeeDetermining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three-camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian-view intersection classification experiments show that our feature fusion model provides an area under the curve and F1-score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three- and one-camera models.https://doi.org/10.4218/etrij.2023-0100augmentationcomputer visiondeep learningintersection classificationtransfer learning |
| spellingShingle | Marcella Astrid Seung-Ik Lee Assembling three one-camera images for three-camera intersection classification ETRI Journal augmentation computer vision deep learning intersection classification transfer learning |
| title | Assembling three one-camera images for three-camera intersection classification |
| title_full | Assembling three one-camera images for three-camera intersection classification |
| title_fullStr | Assembling three one-camera images for three-camera intersection classification |
| title_full_unstemmed | Assembling three one-camera images for three-camera intersection classification |
| title_short | Assembling three one-camera images for three-camera intersection classification |
| title_sort | assembling three one camera images for three camera intersection classification |
| topic | augmentation computer vision deep learning intersection classification transfer learning |
| url | https://doi.org/10.4218/etrij.2023-0100 |
| work_keys_str_mv | AT marcellaastrid assemblingthreeonecameraimagesforthreecameraintersectionclassification AT seungiklee assemblingthreeonecameraimagesforthreecameraintersectionclassification |