用主题模型自动标注交通视频，提升检索准确率。
Content-based Video Retrieval in Traffic Videos using Latent Dirichlet Allocation Topic Model
- 基于LDA主题模型无监督提取视频语义特征
- 相比传统方法，查准率提升124%，误报率降低80%
- 支持灵活查询复杂行为，且存储占用更少
内容驱动的视频检索是监控系统中最具挑战性的任务之一。本研究采用隐含狄利克雷分布（LDA）主题模型对监控视频进行无监督标注。在场景理解方法中，部分学习到的模式存在模糊性，表现为基本动作的混合。为解决该模糊性问题，通过处理特征向量与主模型，构建出描述场景的次级模型，该模型以清晰的原始模式表达，避免歧义。实验表明，在检索任务上性能优于其他基于主题模型的方法。在误报和真报响应方面，分别实现至少80%和124%的改进。本文提出四种搜索策略，用户可基于主题模型定义并检索多种活动。此外，所提方法的轻量级数据库显著减少存储开销，从而加快检索速度，相较于依赖低层特征的方法更具优势。
原文摘要 · Abstract (English)
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.
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