This paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets. A neighborhood rough filter is designed in this process for initial labeling of continuous moving object(s) even in the presence of several variations in different feature spaces. The locations and color models of the object(s) are estimated using their lower–upper approximations in spatio-color neighborhood granular space. Velocity neighborhood granules and acceleration neighborhood granules are then defined over this estimation to predict the object location in the next frame and to speed up the tracking process. A novel concept, namely, intuitionsistic entropy is introduced here, which consists of two new measures: neighborhood rough entropy and neighborhood probabilistic entropy to deal with the ambiguities that arise due to occurrence of overlapping/ occlusion in a video sequence. The unsupervised method of tracking is equally good even when compared with some of the state-of-the art partially supervised methods while showing superior performance during total occlusion.

中文翻译：

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无监督跟踪中的邻域粗滤器和直觉熵

本文旨在通过探索邻域粗糙集的优点，开发一种新的无监督视频跟踪方法。在这个过程中设计了一个邻域粗滤器，即使在不同特征空间中存在几种变化的情况下，也可以对连续运动对象进行初始标记。对象的位置和颜色模型是使用它们在空间颜色邻域粒度空间中的上下近似值来估计的。然后在此估计上定义速度邻域颗粒和加速度邻域颗粒以预测下一帧中的对象位置并加快跟踪过程。这里引入了一个新的概念，即直觉熵，它由两个新的度量组成：邻域粗熵和邻域概率熵来处理由于视频序列中出现重叠/遮挡而引起的歧义。即使与一些最先进的部分监督方法相比，无监督跟踪方法也同样出色，同时在完全遮挡期间表现出卓越的性能。