Detecção Automática de Modelo para Calibração de Câmera

• Autor(a) principal: Dantas, Marrone Silvério Melo
• Data de Publicação: 2022
• Outros Autores: Bezerra, Daniel, Oliveira Filho, Assis T. de, Barbosa, Gibson, Rodrigues, Iago Richard, Sadok, Djamel H. J., Kelner, Judith, Souza, Ricardo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/36168
• topic: Detección de modelos; Calibración de cámara; Aprendizaje profundo.; Template Detection; Camera Calibration; Deep learning.; Detecção de Templates; Calibração de Câmeras; Aprendizagem profunda.

Camera calibration is the process of extract the intrinsic and extrinsic parameters of a camera. Those parameters guide the 3-dimensional localization into relation to the 2-dimensional space from the images acquired by the camera. The 3-dimensional correlation can be generated with an object with known measures, being the most common checkerboard for this purpose. From these checker- boards, the usual approach extracts the position of the inner points, equivalent to the corners of the squares, to generate this correlation. A broad range of algorithms tries to find those points on the image. Still, usually, they require previous knowledge about the dimensions of the image, the pattern distribution, or even the pattern type. In some scenario, maybe is difficult, or impossible, to implement such precise solution, targeting these limitations our work proposes a two-step end-to-end convolutional neural network architecture that processes the corner detection on a unique flow. Our proposal is agnostic to checkerboard size, pattern disposal, and positioning. In our work, first, a segmentation CNN extracts only the checkerboard from the input image (CheckerNet); from the extracted checkerboard, we extract the corner points with a corner detection CNN (Point- Net). The PointNet also works as a segmentation CNN, and the generated points are heatmaps related to points on the checkerboard corners. We performed post-processing with a K-Means-based clustering to convert those heatmaps into single positions (x,y) from the image. We compare our proposed method with the other well-known convolutional neural networks used for corner detection MATE and CCDN. For the evaluation, two datasets were used: GoPro e uEye. Our method provides better results in both datasets, reducing missed corners, double detections, false positives, and competitive results on pixel accuracy.