面向大规模城市监控视频检索的语义属性研究

项目名称： 面向大规模城市监控视频检索的语义属性研究

项目编号： No.61303186

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 孙浩

作者单位： 中国人民解放军国防科学技术大学

项目金额： 26万元

中文摘要： 随着"平安城市"建设的不断深入，监控摄像头已经遍布城市的大街小巷。城市监控网络不断产生着大量的视频数据，如何从这些大规模数据中快速准确地检索用户关心的信息已经成为制约智能监控系统发展和应用的瓶颈问题。 基于语义属性的视觉对象描述与检索方法，可以有效地克服语义鸿沟问题，较准确地将人类的检索意图传达给计算机，同时还具有有效性高、易于并行化等优点，是大规模图像、视频数据集检索研究的新思路。 本项目研究基于语义属性的大规模城市监控视频检索的新理论和新方法，针对城市监控视频大规模、多视角、可量测、复杂动态背景和多模态多配置类型的特点，通过构建引入监控先验信息与时空信息累积的多层次描述性视觉属性模型，推断能够有效地平衡对象的个体区分性、群体相关性、属性可命名性的潜在语义属性，有效地联合量测属性、多层次描述性视觉属性和潜在语义属性描述视频对象，提高面向对象的大规模监控视频检索的性能。

中文关键词： 视频监控；语义属性；视频检索；稀疏因子分析；贝叶斯网络

英文摘要： With the development of video surveillance technology and increasing attention on public safety, nowadays surveillance cameras are around everywhere in our cities. On one hand, a large amount of surveillance videos are continuously being generated by the camera network. On the other, video processing techniques are far short of satisfying human expectations. This is especially true in the case of accurate and effective specific object retrieval in large scale urban surveillance videos. Attribute-based object description and retrieval techniques have received more and more attentions as attributes can overcome the semantic gap, and can communicate user's intent effectively to the computer. Attribute-based representation is also low dimensional and can be easily parallelized for large scale datasets. In this project we focus on new theories and methods of attribute-based object retrieval in large scale urban surveillance videos. The city surveillance videos are often characterized by large-scale, multiple viewpoints, measurable, dynamic backgrounds with complex structures, as well as multi-modal and multi-platform. We propose a novel framework for video object attribute modeling using various cues. Firstly, a hierarchical describable visual attribute learning scheme, which combines prior viewpoint information and

英文关键词： surveillance video；semantic attributes；video retrieval；sparse factor analysis；Bayesian network