State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overlooking adverse atmospheric conditions such as fog, haze, smoke and dust. As a result, the robustness of trackers against these challenging conditions remains underexplored. To address this gap, we introduce physics-based volumetric fog simulation method for arbitrary MOT datasets, utilizing frame-by-frame monocular depth estimation and a fog formation optical model. We enhance our simulation by rendering both homogeneous and heterogeneous fog and propose to use the dark channel prior method to estimate atmospheric light, showing promising results even in night and indoor scenes. We present the leading benchmark MOTChallenge (third release) augmented with fog (smoke for indoor scenes) of various intensities and conduct a comprehensive evaluation of MOT methods, revealing their limitations under fog and fog-like challenges.

翻译：当前最先进的多目标跟踪方法在现有基准数据集上进行训练和评估时表现出卓越的性能。然而，这些基准数据集主要由晴朗天气场景构成，忽略了雾、霾、烟雾和灰尘等不利大气条件。因此，跟踪器在这些挑战性条件下的鲁棒性仍未得到充分探索。为弥补这一空白，我们提出了一种基于物理的、适用于任意多目标跟踪数据集的体积雾模拟方法，该方法利用逐帧单目深度估计和雾形成光学模型。我们通过渲染均匀与非均匀雾效来增强模拟效果，并建议使用暗通道先验方法估计大气光，即使在夜间和室内场景中也显示出有前景的结果。我们呈现了领先的基准数据集MOTChallenge（第三版）的雾化增强版本（室内场景使用烟雾模拟），包含多种强度等级，并对多目标跟踪方法进行了全面评估，揭示了其在雾及类雾挑战下的局限性。