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。