| 제목 |
CNN 기반 차량 밑면 중심점 검출을 통한 차량 위치 추정 정확도 개선 |
| 분야 |
ITS/차량 소프트웨어 |
| 언어 |
Korean |
| 저자 |
김가현(세종대학교), 정호기(한국교통대학교), 서재규(세종대학교) |
| Key Words |
Vehicle position estimation(차량 위치 추정), Bottom face center(밑면 중심점), Deep neural network(딥뉴럴네트워크), Vehicle-to-infrastructure communication(V2I 통신), Surveillance camera(감시 카메라) |
| 초록 |
This paper proposes a method to improve vehicle position accuracy by detecting its bottom face center(BFC) based on a convolutional neural network. The proposed method is implemented by simply adding only the heads for BFC detection after maintaining the architecture of the existing vehicle detector. The BFC is calculated based on the feature map obtained from the vehicle detector. In order for the network to detect the BFC, the origin and distance function must be defined. In this paper, eight combinations of two methods for the origin, and four methods for the distance function were compared and evaluated. The performance of the proposed method is quantitatively evaluated by using Euclidean distance error and normalized Euclidean distance error. The result of this study revealed that the proposed method shows a 94.6 % improvement in vehicle position accuracy, compared to the previous method. |
| 원문(PDF) |
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