21,896 research outputs found
Uncertainty Estimation in One-Stage Object Detection
Environment perception is the task for intelligent vehicles on which all
subsequent steps rely. A key part of perception is to safely detect other road
users such as vehicles, pedestrians, and cyclists. With modern deep learning
techniques huge progress was made over the last years in this field. However
such deep learning based object detection models cannot predict how certain
they are in their predictions, potentially hampering the performance of later
steps such as tracking or sensor fusion. We present a viable approaches to
estimate uncertainty in an one-stage object detector, while improving the
detection performance of the baseline approach. The proposed model is evaluated
on a large scale automotive pedestrian dataset. Experimental results show that
the uncertainty outputted by our system is coupled with detection accuracy and
the occlusion level of pedestrians
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks such as
object pose estimation when the input image is of high quality. However, in
challenging imaging conditions such as on low-resolution images or when the
image is corrupted by imaging artifacts, current systems degrade considerably
in accuracy. While a loss in performance is unavoidable, we would like our
models to quantify their uncertainty in order to achieve robustness against
images of varying quality. Probabilistic deep learning models combine the
expressive power of deep learning with uncertainty quantification. In this
paper, we propose a novel probabilistic deep learning model for the task of
angular regression. Our model uses von Mises distributions to predict a
distribution over object pose angle. Whereas a single von Mises distribution is
making strong assumptions about the shape of the distribution, we extend the
basic model to predict a mixture of von Mises distributions. We show how to
learn a mixture model using a finite and infinite number of mixture components.
Our model allows for likelihood-based training and efficient inference at test
time. We demonstrate on a number of challenging pose estimation datasets that
our model produces calibrated probability predictions and competitive or
superior point estimates compared to the current state-of-the-art
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