航空场景中移动对象异常行为自动识别技术研究

项目名称： 航空场景中移动对象异常行为自动识别技术研究

项目编号： No.U1233119

项目类型： 联合基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 刘芳

作者单位： 华中科技大学

项目金额： 38万元

中文摘要： 目前候机厅、停机场和机舱等航空场景中异常事件的识别由人工完成，缺乏主动性和实时性，自动识别场景异常亟待研究，而场景异常通常由移动对象的行为体现，因此需要研究移动对象异常行为自动识别技术。移动对象的行为由其自身运动及与其它对象的互动体现，针对前者，基于混合动态纹理的模型效果好，但运算极耗时，本项目拟研究刻画能力强且具可扩展性的特征以实现快速建模与检测；同时研究对象间、对象与场景的互动模式。现有自动识别方法一般利用信号特征提取高级语义，语义鸿沟决定了其局限性，本项目结合自顶向下框架，研究交互式异常行为识别及其敏感度评价模型，利用交互式技术引入航空专家对异常的描述，研究场景特征与标定的异常行为及其敏感程度之间的映射模型，其中场景特征由场景模型描述，该模型应能表达或支持计算对象运动行为、互动行为以及场景其它属性。本项目的研究可对维护航空安全提供基础技术，将推动航空领域自动视频识别技术的发展。

中文关键词： 民航场景视频监控；动作识别；对象跟踪；流形特征；高斯混合模型

英文摘要： At present, abnormal event occurs in scenes such as departure lounge in airport, apron and cabin of the plane is detected from monitoring videos manually. However automatic method is needed for initiative and real-time requirements. Abnormal event is normally measured by the action of moving objects, and it can be detected through behavior of moving objects which expressed by its own action and the interaction between objects. Mixture Dynamic Texture model is effective in abnormal detection from the action of moving objects, but it is time consuming. This proposal aims to improve the efficiency by presenting new features with scalability and description, and also study the interactive pattern between objects and scene. The existing automatic abnormal detection methods try to extract high level semantic information such as ''abnormal'' from signals, which is limited due to the semantic gap. This proposal introduces interactive technology to define the ''abnormal'' by human experts. Then a top-down scheme is used in the mapping model among scene, abnormal behavior and sensitivity degree of abnormal. The scene is described by a proposed scene model which expresses action of moving objects, interactive behavior and other properties of the scene. The methods proposed in this proposal provide fundamental technology fo

英文关键词： Commercial Aviation Scene Video Monitoring；action recognition；object tracking；manifold features；Gaussian Mixture Model