Object tracking in a real-life scenario is very challenging due to occlusion. State-space models like Kalman and particle filters are well known to handle such a particular problem. The particle filter’s performance for solving such a problem depends on two issues - motion model and observation (i.e., likelihood) model. The question remains to exist due to the lack of useful observation and efficient motion models. This article presents an impressive observation model based on confidence (classification) score provided by introducing object-background prototypes based discriminative model. The proposed discriminative model is constructed with the prior knowledge of two classes (i.e., object and background) and tries to discriminate between three categories: an object, background, and occluded part of that object. The existing composite motion model handles the object motion and its scale. We also propose a model update technique that adapts the appearance changes of the object during tracking. We evaluate the proposed method on several challenging benchmark sequences. Analysis of the results concludes that the proposed technique can track fully (or partially) occluded object and the object in various complex environments.

由于存在遮挡，在现实生活中进行对象跟踪非常具有挑战性。众所周知，诸如卡尔曼和粒子滤波器之类的状态空间模型可以处理此类特定问题。粒子滤波器用于解决此类问题的性能取决于两个问题-运动模型和观察（即，可能性）模型。由于缺乏有用的观察和有效的运动模型，这个问题仍然存在。本文介绍了一种基于置信度（分类）评分的令人印象深刻的观察模型，通过引入基于对象背景原型的判别模型提供了这种评分。所提出的判别模型是利用两类（即对象和背景）的先验知识构建的，并试图区分三类：对象，背景和该对象的遮挡部分。现有的复合运动模型处理对象运动及其比例。我们还提出了一种模型更新技术，该技术可以在跟踪过程中适应对象的外观变化。我们在几个具有挑战性的基准序列上评估了提出的方法。对结果的分析得出结论，所提出的技术可以完全（或部分）跟踪被遮挡的物体以及各种复杂环境中的物体。