Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer vision-based automatic surveillance scene analysis is carried out in unsupervised ways. Topic modelling is one of the emerging fields used in unsupervised information processing. Topic modelling is used in text analysis, computer vision applications, and other areas involving spatio-temporal data. In this article, we discuss the scope, variations, and applications of topic modelling, particularly focusing on surveillance video analysis. We have provided a methodological survey on existing topic models, their features, underlying representations, characterization, and applications in visual surveillance’s perspective. Important research papers related to topic modelling in visual surveillance have been summarized and critically analyzed in this article.

中文翻译：

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基于主题的视频分析

由于各种原因，手动处理通过闭路电视捕获的大量视频数据具有挑战性。首先，人工分析非常耗时。此外，由于监控视频是在动态条件下记录的，例如存在摄像机运动、变化的照明或遮挡的情况下，传统的监督学习可能并不总是有效。因此，基于计算机视觉的自动监控场景分析是以无监督的方式进行的。主题建模是无监督信息处理中使用的新兴领域之一。主题建模用于文本分析、计算机视觉应用和其他涉及时空数据的领域。在本文中，我们讨论了主题建模的范围、变化和应用，特别关注监控视频分析。我们提供了关于现有主题模型、它们的特征、基础表示、表征和视觉监控视角中的应用的方法论调查。本文对与视觉监控中的主题建模相关的重要研究论文进行了总结和批判性分析。