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arXiv · 1811.11210

Calibrating Uncertainties in Object Localization Task

Buu Phan·Rick Salay·Krzysztof Czarnecki·Vahdat Abdelzad·Taylor Denouden·Sachin Vernekar
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Abstract

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confidence interval for its bounding box. While recent Bayesian deep learning methods provide a principled way to estimate this uncertainty, the estimates for the bounding boxes obtained using these methods are uncalibrated. In this paper, we address this problem for the single-object localization task by adapting an existing technique for calibrating regression models. We show, experimentally, that the resulting calibrated model obtains more reliable uncertainty estimates.

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Buu Phan, Rick Salay, Krzysztof Czarnecki, Vahdat Abdelzad, Taylor Denouden, Sachin Vernekar. 2018-11-27. Calibrating Uncertainties in Object Localization Task. https://arxiv.org/abs/1811.11210

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