Abstract
Content-based video retrieval is one of the most challenging tasks in surveillance systems. In this study, Latent Dirichlet Allocation (LDA) topic model is used to annotate surveillance videos in an unsupervised manner. In scene understanding methods, some of the learned patterns are ambiguous and represents a mixture of atomic actions. To address the ambiguity issue in the proposed method, feature vectors, and the primary model are processed to obtain a secondary model which describes the scene with primitive patterns that lack any ambiguity. Experiments show performance improvement in the retrieval task compared to other topic model-based methods. In terms of false positive and true positive responses, the proposed method achieves at least 80\% and 124\% improvement respectively. Four search strategies are proposed, and users can define and search for a variety of activities using the proposed query formulation which is based on topic models. In addition, the lightweight database in our method occupies much fewer storage which in turn speeds up the search procedure compared to the methods which are based on low-level features.
Abstract (translated)
基于内容的视频检索是监控系统中最具挑战性的任务之一。在这项研究中，利用潜在狄利克雷分配（Latent Dirichlet Allocation, LDA）主题模型以无监督的方式为监控视频进行标注。在场景理解方法中，一些学习到的模式存在模糊性，并且代表了一系列基本动作的混合体。为了应对提出的这种方法中的模糊问题，通过对特征向量和初始模型的处理来获得一个次级模型，该模型用原始而没有模糊性的模式描述场景。实验结果显示，在检索任务中与基于其他主题模型的方法相比性能得到了提升。在假阳性响应和真阳性响应方面，所提出的方法至少分别提高了80%和124%。提出了四种搜索策略，并且用户可以使用基于主题模型的查询形式定义并查找各种活动。此外，我们方法中的轻量级数据库占用了更少的存储空间，从而比基于低级别特征的方法加快了检索过程的速度。
URL
https://arxiv.org/abs/2502.05457
