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The accuracy of camera calibration depends significantly on the control point detection which customarily subjects to image noise, distortion and resolution. This paper uses concentric circle grids as the calibration pattern and accordingly proposes a robust and accurate control point detection method for camera calibration. Each set of concentric circles on the grids pattern consists of multiple circles whose radii decrease progressively. The geometric structure of multi-hierarchies can not only robustly extract control points from complicated scenarios but also build a voting scheme to avoid numerical instability resulting from the estimation of projected centers. According to the common self-polar triangle of concentric circles and the local sub-pixel edge detection algorithm, we efficiently estimated sub-pixel coordinates of control points under projective transformation. Simulations and real experiments were conducted to demonstrate that the performance of calibration using concentric circle grids is superior to the popular grid patterns with respect to the image noise, distortion and resolution.