Multi-object tracking (MOT) methods have seen a significant boost in
performance recently, due to strong interest from the research community and
steadily improving object detection methods. The majority of tracking methods
follow the tracking-by-detection (TBD) paradigm, blindly trust the incoming
detections with no sense of their associated localization uncertainty. This
lack of uncertainty awareness poses a problem in safety-critical tasks such as
autonomous driving where passengers could be put at risk due to erroneous
detections that have propagated to downstream tasks, including MOT. While there
are existing works in probabilistic object detection that predict the
localization uncertainty around the boxes, no work in 2D MOT for autonomous
driving has studied whether these estimates are meaningful enough to be
leveraged effectively in object tracking. We introduce UncertaintyTrack, a
collection of extensions that can be applied to multiple TBD trackers to
account for localization uncertainty estimates from probabilistic object
detectors. Experiments on the Berkeley Deep Drive MOT dataset show that the
combination of our method and informative uncertainty estimates reduces the
number of ID switches by around 19\% and improves mMOTA by 2-3%. The source
code is available at this https URL

基于概率目标检测的定位不确定性估计的 2D MOT 方法在自动驾驶中能有效减少误检以及提升 MOTA。

UncertaintyTrack: 在多目标追踪中利用检测和定位的不确定性

UncertaintyTrack: Exploiting Detection and Localization Uncertainty in  Multi-Object Tracking

The superior performance of object detectors is often established under the
condition that the test samples are in the same distribution as the training
data. However, in many practical applications, out-of-distribution (OOD)
instances are inevitable and usually lead to uncertainty in the results. In
this paper, we propose a novel, intuitive, and scalable probabilistic object
detection method for OOD detection. Unlike other uncertainty-modeling methods
that either require huge computational costs to infer the weight distributions
or rely on model training through synthetic outlier data, our method is able to
distinguish between in-distribution (ID) data and OOD data via weight parameter
sampling from proposed Gaussian distributions based on pre-trained networks. We
demonstrate that our Bayesian object detector can achieve satisfactory OOD
identification performance by reducing the FPR95 score by up to 8.19% and
increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC
datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD
dataset.

基于预训练网络的提出高斯分布，通过权重参数抽样区分正常数据和超出分布的数据，证明我们的贝叶斯目标检测器在 BDD100k 和 VOC 数据集上的训练，并在 COCO2017 数据集上的评估中通过降低 FPR95 分数最多 8.19％和增加 AUROC 分数最多 13.94％来实现了令人满意的超出分布的辨别性能。

贝叶斯不确定性估计在离域物体检测中的应用

Out-of-distribution Object Detection through Bayesian Uncertainty  Estimation

Predictive uncertainty estimation is an essential next step for the reliable
deployment of deep object detectors in safety-critical tasks. In this work, we
focus on estimating predictive distributions for bounding box regression output
with variance networks. We show that in the context of object detection,
training variance networks with negative log likelihood (NLL) can lead to high
entropy predictive distributions regardless of the correctness of the output
mean. We propose to use the energy score as a non-local proper scoring rule and
find that when used for training, the energy score leads to better calibrated
and lower entropy predictive distributions than NLL. We also address the
widespread use of non-proper scoring metrics for evaluating predictive
distributions from deep object detectors by proposing an alternate evaluation
approach founded on proper scoring rules. Using the proposed evaluation tools,
we show that although variance networks can be used to produce high quality
predictive distributions, ad-hoc approaches used by seminal object detectors
for choosing regression targets during training do not provide wide enough data
support for reliable variance learning. We hope that our work helps shift
evaluation in probabilistic object detection to better align with predictive
uncertainty evaluation in other machine learning domains. Code for all models,
evaluation, and datasets is available at:
this https URL

本研究旨在估计具有方差网络的边界框回归输出的预测分布，使用 proper scoring rules 训练 variance networks 可以产生更好的预测分布，虽然变量网络可以用于生成高质量的预测分布，但我们的提议可帮助将基于规范分布模型的预测不确定性评估与其他机器学习领域的预测评估对齐。

深度目标检测器中回归预测不确定性的估计与评估

Estimating and Evaluating Regression Predictive Uncertainty in Deep  Object Detectors

We introduce Probabilistic Object Detection, the task of detecting objects in
images and accurately quantifying the spatial and semantic uncertainties of the
detections. Given the lack of methods capable of assessing such probabilistic
object detections, we present the new Probability-based Detection Quality
measure (PDQ).Unlike AP-based measures, PDQ has no arbitrary thresholds and
rewards spatial and label quality, and foreground/background separation quality
while explicitly penalising false positive and false negative detections. We
contrast PDQ with existing mAP and moLRP measures by evaluating
state-of-the-art detectors and a Bayesian object detector based on Monte Carlo
Dropout. Our experiments indicate that conventional object detectors tend to be
spatially overconfident and thus perform poorly on the task of probabilistic
object detection. Our paper aims to encourage the development of new object
detection approaches that provide detections with accurately estimated spatial
and label uncertainties and are of critical importance for deployment on robots
and embodied AI systems in the real world.

介绍一种新的概率目标检测方法，可提供准确的空间和语义不确定性，与现有方法进行对比，证明传统目标检测方法在空间上犯有严重错误，并鼓励开发新的对象检测方法以在实际场景中实现机器人和具体化的人工智能系统。