In this paper, we propose a novel approach for integrating multiple tracking cues within a unified probabilistic graph-based Markov random fields (MRFs) representation. We show how to integrate temporal and spatial cues encoded by unary and pairwise probabilistic potentials. As the inference of such high-order MRF models is known to be NP-hard, we propose an efficient spectral relaxation-based inference scheme. The proposed scheme is exemplified by applying it to a mixture of five tracking cues, and is shown to be applicable to wider sets of cues. This paves the way for a modular plug-and-play tracking framework that can be easily adapted to diverse tracking scenarios. The proposed scheme is experimentally shown to compare favorably with contemporary state-of-the-art schemes, and provides accurate tracking results.

中文翻译：

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基于概率图的即插即用多线索视觉跟踪框架。

在本文中，我们提出了一种在基于概率图的统一马尔可夫随机场（MRF）表示中集成多个跟踪线索的新颖方法。我们展示了如何整合由一元和成对概率势编码的时空线索。由于已知此类高阶MRF模型的推论是NP难的，因此我们提出了一种有效的基于频谱弛豫的推论方案。通过将其应用于五个跟踪线索的混合物来举例说明该方案，并且该方案显示适用于更广泛的线索集。这为模块化即插即用跟踪框架铺平了道路，该框架可以轻松适应各种跟踪方案。实验表明，所提出的方案可与当代最新技术方案相媲美，并提供准确的跟踪结果。