Reliable spatial uncertainty evaluation of object detection models is of special interest and has been subject of recent work. In this work, we review the existing definitions for uncertainty calibration of probabilistic regression tasks. We inspect the calibration properties of common detection networks and extend state-of-the-art recalibration methods. Our methods use a Gaussian process (GP) recalibration scheme that yields parametric distributions as output (e.g. Gaussian or Cauchy). The usage of GP recalibration allows for a local (conditional) uncertainty calibration by capturing dependencies between neighboring samples. The use of parametric distributions such as as Gaussian allows for a simplified adaption of calibration in subsequent processes, e.g., for Kalman filtering in the scope of object tracking. In addition, we use the GP recalibration scheme to perform covariance estimation which allows for post-hoc introduction of local correlations between the output quantities, e.g., position, width, or height in object detection. To measure the joint calibration of multivariate and possibly correlated data, we introduce the quantile calibration error which is based on the Mahalanobis distance between the predicted distribution and the ground truth to determine whether the ground truth is within a predicted quantile. Our experiments show that common detection models overestimate the spatial uncertainty in comparison to the observed error. We show that the simple Isotonic Regression recalibration method is sufficient to achieve a good uncertainty quantification in terms of calibrated quantiles. In contrast, if normal distributions are required for subsequent processes, our GP-Normal recalibration method yields the best results. Finally, we show that our covariance estimation method is able to achieve best calibration results for joint multivariate calibration.
翻译:对物体探测模型的可靠空间不确定性评估是特别感兴趣的,是最近工作的主题。在这项工作中,我们审查了对概率回归任务进行不确定性校准的不确定性现有定义。我们检查了共同探测网络的校准特性,并扩展了最先进的校准方法。我们的方法使用高氏进程(GP)校准办法,得出参数分布作为输出(如高萨或高萨)。使用GP校正校准允许通过获取相邻样本之间的依赖性来进行局部(有条件)不确定性校准。使用诸如高沙仪等参数分布法,可以简化共同探测网络网络的校准特性,并推广最先进的校准方法。此外,我们使用GGP校正校准办法进行测校准,以得出输出量的参数,例如,直径校准的数值,在目标检测中,我们的标准值、直径比值、位置、宽度或高度。测量多变校准的比值分布法,用于测量多变差和可能的校准分布法的校正法,我们采用最精确法,以显示我们测算法的测测算法,最精确的测算法,其测算法显示我们测算法是测测测测得的。