Abstract
Camera calibration is a prerequisite for many computer vision applications. While a good calibration can turn a camera into a measurement device, it can also deteriorate a system’s performance if not done correctly. In the recent past, there have been great efforts to simplify the calibration process. Yet, inspection and evaluation of calibration results typically still requires expert knowledge.
In this work, we introduce two novel methods to capture the fundamental error sources in camera calibration: systematic errors (biases) and remaining uncertainty (variance). Importantly, the proposed methods do not require capturing additional images and are independent of the camera model. We evaluate the methods on simulated and real data and demonstrate how a state-of-the-art system for guided calibration can be improved. In combination, the methods allow novice users to perform camera calibration and verify both the accuracy and precision.
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Notes
- 1.
Here, we assume the underlying distribution is Gaussian but might be subject to sporadic outliers. The MAD multiplied by a factor of 1.4826 gives a robust estimate for the standard deviation [14].
- 2.
To choose a threshold, it can be used that \(\frac{1}{1-\mathrm {BR}}\) is approximately F-distributed, representing the ratio of the residual sum of squares (SSE) of the calibration over the SSE of the virtual targets, weighted by their respective degrees of freedom. However, this only holds approximately, as the datapoints are not independent. We therefore use an empirical threshold of \(\tau _{BR}=0.2\), allowing for small biases.
- 3.
The decomposition of the target must lead to an overdetermined estimation problem.
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Hagemann, A., Knorr, M., Janssen, H., Stiller, C. (2021). Bias Detection and Prediction of Mapping Errors in Camera Calibration. In: Akata, Z., Geiger, A., Sattler, T. (eds) Pattern Recognition. DAGM GCPR 2020. Lecture Notes in Computer Science(), vol 12544. Springer, Cham. https://doi.org/10.1007/978-3-030-71278-5_3
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