Robust visual tracking plays a vital role in many areas such as autonomous cars, surveillance and robotics. Recent trackers were shown to achieve adequate results under normal tracking scenarios with clear weather conditions, standard camera setups and lighting conditions. Yet, the performance of these trackers, whether they are correlation filter-based or learning-based, degrade under adverse weather conditions. The lack of videos with such weather conditions, in the available visual object tracking datasets, is the prime issue behind the low performance of the learning-based tracking algorithms. In this work, we provide a new person tracking dataset of real-world sequences (PTAW172Real) captured under foggy, rainy and snowy weather conditions to assess the performance of the current trackers. We also introduce a novel person tracking dataset of synthetic sequences (PTAW217Synth) procedurally generated by our NOVA framework spanning the same weather conditions in varying severity to mitigate the problem of data scarcity. Our experimental results demonstrate that the performances of the state-of-the-art deep trackers under adverse weather conditions can be boosted when the available real training sequences are complemented with our synthetically generated dataset during training.

强大的视觉跟踪在自动驾驶，监视和机器人等许多领域都发挥着至关重要的作用。事实证明，在天气晴朗，标准摄像机设置和光照条件正常的情况下，最新的追踪器可以取得足够的效果。然而，这些跟踪器的性能，无论是基于相关过滤器还是基于学习的，都会在不利的天气条件下下降。在可用的视觉对象跟踪数据集中缺少具有这种天气条件的视频，是基于学习的跟踪算法性能低下的主要问题。在这项工作中，我们提供了在有雾，下雨和下雪的天气条件下捕获的真实世界序列（PTAW172Real）的新人员跟踪数据集，以评估当前跟踪器的性能。我们还介绍了一个新颖的人跟踪数据集，该数据集是由我们的NOVA框架程序生成的，其合成序列（PTAW217Synth）跨越相同的天气条件，严重程度有所不同，以缓解数据稀缺问题。我们的实验结果表明，如果在训练过程中将可用的实际训练序列与我们综合生成的数据集相辅相成，则可以提高恶劣天气条件下最先进的深层跟踪器的性能。