Visual tracking has been an active research area in computer vision for decades. However, the performance of existing techniques is still challenged by various factors, such as occlusion and change in appearance of the target. In this paper, we propose a novel framework based on correlation filtering and probabilistic finite state machines (FSMs) to handle occlusion. In our tracking framework, the target is partitioned into several parts whose occlusion states are automatically detected. A set of states for the target is defined in terms of the combination of the parts’ occlusion states. The probabilistic FSMs are then used to model the target’s state transitions so as to reduce the effect of noise in the output response maps of correlation filters. Our target model’s update strategy is adaptable online depending on the estimated state of the target. Extensive experiments have been performed on several public benchmarks and the proposed algorithm achieves competitive results against state-of-the-art techniques.

中文翻译：

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通过概率有限状态机进行视觉跟踪中的遮挡处理

几十年来，视觉跟踪一直是计算机视觉领域的活跃研究领域。但是，现有技术的性能仍然受到各种因素的挑战，例如目标的遮挡和外观变化。在本文中，我们提出了一种基于相关过滤和概率有限状态机（FSM）来处理遮挡的新颖框架。在我们的跟踪框架中，目标被分为几个部分，这些部分的遮挡状态会自动检测到。根据零件的遮挡状态的组合来定义目标的一组状态。然后使用概率FSM对目标的状态转换进行建模，以减少相关滤波器的输出响应图中的噪声影响。我们的目标模型的更新策略可根据目标的估计状态进行在线调整。已经在几个公共基准上进行了广泛的实验，并且所提出的算法与最先进的技术相比具有竞争优势。