The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes. Probabilistic tracking algorithms, which are leading in other fields, are surprisingly absent from the leaderboards. We found that accounting for distance in target kinematics, exploiting detector confidence and modelling non-uniform clutter characteristics is critical for a probabilistic tracker to work in visual tracking. Previous probabilistic methods fail to address most or all these aspects, which we believe is why they fall so far behind current state-of-the-art (SOTA) methods (there are no probabilistic trackers in the MOT17 top 100). To rekindle progress among probabilistic approaches, we propose a set of pragmatic models addressing these challenges, and demonstrate how they can be incorporated into a probabilistic framework. We present BASE (Bayesian Approximation Single-hypothesis Estimator), a simple, performant and easily extendible visual tracker, achieving state-of-the-art (SOTA) on MOT17 and MOT20, without using Re-Id. Code will be made available at https://github.com/ffi-no

翻译：摘要：视觉目标跟踪领域主要被结合简单跟踪算法与特定规则方案的方法所主导。令人意外的是，在其他领域处于领先地位的概率跟踪算法在排行榜上却鲜有出现。我们发现，在目标运动学中考虑距离因素、利用检测器置信度以及建模非均匀杂波特征，对于概率跟踪器在视觉跟踪中的有效性至关重要。以往的概率方法未能全面或部分解决这些方面的问题，我们认为这正是它们远落后于当前最先进方法的原因（在MOT17前100名中不存在任何概率跟踪器）。为重新推动概率方法的发展，我们提出一组应对上述挑战的实用模型，并展示了如何将其整合到概率框架中。我们提出BASE（贝叶斯近似单假设估计器），这是一种简单、高效且易于扩展的视觉跟踪器，在不使用重识别的情况下，在MOT17和MOT20上均达到了最先进水平。代码将发布于 https://github.com/ffi-no