State-of-the-art (SOTA) trackers have shown remarkable Multiple Object
Tracking (MOT) performance when trained and evaluated on current benchmarks.
However, these benchmarks primarily consist of clear scenarios, overlooking
adverse atmospheric conditions such as fog, haze, smoke and dust. As a result,
the robustness of SOTA trackers remains underexplored. To address these
limitations, we propose a pipeline for physic-based volumetric fog simulation
in arbitrary real-world MOT dataset utilizing frame-by-frame monocular depth
estimation and a fog formation optical model. Moreover, we enhance our
simulation by rendering of both homogeneous and heterogeneous fog effects. We
propose to use the dark channel prior method to estimate fog (smoke) color,
which shows promising results even in night and indoor scenes. We present the
leading tracking benchmark MOTChallenge (MOT17 dataset) overlaid by fog (smoke
for indoor scenes) of various intensity levels and conduct a comprehensive
evaluation of SOTA MOT methods, revealing their limitations under fog and
fog-similar challenges.

通过基于物理的体积雾模拟以及逐帧单目深度估计和雾成像光学模型，在多个不同强度的雾中对领先的多目标跟踪方法进行全面评估，揭示了它们在雾和类似雾的挑战下的局限性。

迷雾中的评估：多目标跟踪鲁棒性

Into the Fog: Evaluating Multiple Object Tracking Robustness

This work addresses the challenging task of LiDAR-based 3D object detection
in foggy weather. Collecting and annotating data in such a scenario is very
time, labor and cost intensive. In this paper, we tackle this problem by
simulating physically accurate fog into clear-weather scenes, so that the
abundant existing real datasets captured in clear weather can be repurposed for
our task. Our contributions are twofold: 1) We develop a physically valid fog
simulation method that is applicable to any LiDAR dataset. This unleashes the
acquisition of large-scale foggy training data at no extra cost. These
partially synthetic data can be used to improve the robustness of several
perception methods, such as 3D object detection and tracking or simultaneous
localization and mapping, on real foggy data. 2) Through extensive experiments
with several state-of-the-art detection approaches, we show that our fog
simulation can be leveraged to significantly improve the performance for 3D
object detection in the presence of fog. Thus, we are the first to provide
strong 3D object detection baselines on the Seeing Through Fog dataset. Our
code is available at www.trace.ethz.ch/lidar_fog_simulation.

本研究采用真实数据模拟雾天环境，提出一种适用于任何 LiDAR 数据集的物理真实雾模拟方法。实验结果表明，我们的方法可以明显提高雾天环境下的 3D 目标检测性能，并为通过现有真实数据集提供了强有力的检测结果。