自动行为分析中动态主题建模的学习方法
Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis
摘要：
半监督和无监督系统为操作员提供了宝贵的支持，并可以大大减少操作员的负担。鉴于需要处理大量的视频数据，并提供自主决策，这项工作提出了新的学习算法的活动分析视频。活动和行为由动态主题模型描述。提出了基于期望最大化方法和变分贝叶斯推理的两种新的学习算法。给出了模型参数后验的理论推导。所设计的学习算法进行了比较，在文献中介绍的吉布斯抽样推理计划。一个详细的比较的学习算法的真实视频数据。我们还提出了一个异常定位程序，优雅地嵌入在主题建模框架。拟议的框架可适用于若干领域，包括运输系统、安全和监视。
英文摘要：
Semi-supervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this work proposes new learning algorithms for activity analysis in video. The activities and behaviours are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximisation approach and variational Bayes inference are proposed. Theoretical derivations of the posterior of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localisation procedure, elegantly embedded in the topic modeling framework. The proposed framework can be applied to a number of areas, including transportation systems, security and surveillance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
