【作者】 肖小玲；

【导师】 李腊元；

【作者基本信息】 武汉理工大学 ， 计算机应用技术， 2008， 博士

【摘要】 随着网络及传感器技术的发展,视觉监控系统正向自动化、智能化与普适化方向的发展。现存的视觉监控系统大多仍然只具有采集、存储视频数据的功能,对于实时事件检测任务则交给在监视器前面的监控人员来完成。智能视觉监控系统不应只是具有事故的调查功能,而且还应该具有阻止潜在灾难事故发生的功能。分布式智能视觉监控存在着许多与目前的视觉监控系统不同的新问题和挑战。其中包括系统软件平台、事件挖掘与分析等。本文就分布式智能视觉监控中的某一些关键技术进行了讨论。主要研究工作及创新性体现在以下几个方面:1)基于服务质量的分布式Multi-Agent模型的软件平台针对目前的视觉监控系统中软件平台简单,功能单一,尚存在较多不完善的问题。本文设计了一种基于服务质量QoS的分布式Multi-Agent模型的视觉监控系统软件平台。该平台中各个Agent都是自主的进程,能够可有序一致的互相协调通信。采用引用不耦合、时间上耦合的基于消息组的发布/订阅模型,以利于实现对Agent间的动态性的适应和自发交互。根据分布式智能视觉监控系统所传输的数据类型与特点,提出了一种基于服务质量的数据传输机制。实验测试表明,该传输机制具有较小的数据传输时延。2)面向室内监控场景中前景目标稳定检测与跟踪针对室内监控场景中前景目标的持续检测以及鬼影的去除问题。本文提出了一种面向室内监控场景中前景目标稳定检测方法。该方法采用基于颜色和梯度信息融合、双重背景模型及其更新机制实现对动态的前景目标持续稳定地检测,消除了前景检测中出现的鬼影影响,较好地解决了室内监控场景中目标经常处于“停停走走”,目标比较大且目标各部分运动状态不一致等情况下的目标检测问题。采用基于粒子滤波的概率跟踪框架,选择基于Particle Filter粒子滤波跟踪方法对监控场景中目标进行跟踪,实现了多目标稳定地跟踪。实验结果表明:不管人是静止还是运动的,都可以同时对多人整体、人头以及手进行稳定地检测与跟踪。3)基于概率支持向量机方法的人脸识别方法针对传统支持向量机方法不提供后验概率的输出,建议了“1对1”多类支持向量机的概率建模方法,提高了支持向量机的分类性能。结合了SVM分类器与概率建模的优点,提出了一种基于概率支持向量机方法的人脸识别方法。针对智能会议场景对人脸识别的特殊情况,通过依据检测、跟踪得到头部区域与人脸区域的长度或面积比,选择正面的人脸进行识别,从而降低了人脸姿态对人脸识别的影响。实验结果表明:该方法不仅使人脸识别的精度得到了提高,还提供了其属于所在类中的可信程度。4)基于多层事件融合的场景事件实时分析文中给出了一种基于事件的语义描述定义,描述了事件的三种特性:(1)事件是描述时空过程的语义对象;(2)事件定义取决于场景;(3)事件具有层次性的结构。为解决智能监控场景中场景事件实时分析问题,提出了一种基于多层事件融合的场景事件分析模型以及对应的RBPF实时推理方法。利用事件具有层次结构特性以及各层事件之间的关系,将场景事件分解为不同层次的子事件,利用多层子事件融合进行场景事件分析,并以多层动态贝叶斯网络模型对其建模。构建该模型相应的RBPF推理方法,以实现对复杂场景事件进行实时分析。实验结果表明:该方法能够对动态场景中的场景事件进行实时推理,比PF方法具有更高的精度及较少的时间代价。总之,以上研究成果与创新内容较好地解决了智能监控领域中的几个关键性技术问题,具有广泛的应用前景和潜在的经济价值。本文得到国家自然科学基金项目(90304018,60672137),教育部博士点基金项目(20060497015)和湖北省自然科学基金项目(2004ABA043)的资助。更多还原

【Abstract】 With the development of the network and sensor technologies, automatic and intelligent visual surveillance systems have become more and more popular. Existing visual surveillance systems provide the infrastructure only to capture, store and distribute video, while leaving the task of threat detection exclusively to human operators. Intelligent visual surveillance systems should create a shift in the security paradigm from "investigation of incidents" to "prevention of potentially catastrophic incidents". There exist some open problems and new challenges including software platform, event analysis, and etc., which are different from those in the existing visual surveillance systems. This dissertation addresses some key technologies in intelligent visual surveillance systems and has made innovative progress in the following respects:1) A Distributed Multi-Agent Software Platform based on Quality of ServiceA distributed multi-agent software platform based on quality of service is proposed. Each agent in the software architecture is an independent process, and can consistently communicate each other. The loose-coupled publish-subscribe model based on group messages is adopted, which caters for the spontaneous interaction between modules. A data transmitting mechanism based on quality of service is proposed according to the transmitting data types and features in the distributed intelligent visual surveillance systems. Experiment tests show that this mechanism has less delay for data transmitting.2) Consistent Foreground Object Detection and Tracking for the Indoor ScenesAn algorithm of consistent foreground object detection for the indoor scenes is presented for foreground object detection tracking and eliminating ghost effect. The basic idea of this method is as follows: both color and gradient information are fused, and two background models, the original background and the runtime background, are created and dynamically updated in whole detection process. This method can efficiently solve the problem of consistent foreground object detection for the objects in a "move and stop" way. The tracking method based on the particle filter method can robustly track multiple objects. Experiment results show that this tracking method can simultaneously track the whole body, head and hand if person stop or move.3) Face Recognition Based on the Probability Outputs of Multi-class Support Vector MachinesStandard Support Vector Machines (SVMs) does not provide probabilities output, a directly solving posterior probability method is presented for the probability outputs of’ one against one’ multi-class SVMs, which improves the classification ability. Face recognition based on the probability outputs of multi-class SVMs is proposed combining the advantages both SVM and probability modeling. Considering the special situation for face recognition in intelligent meeting scene, the front face is chosen for face recognition to reduce the pose effect, which is the ratio of the lengths or areas between the head and the face. Experiment results show that this method not only improves the precision of face recognition, but also provides the reliability of the classification.4) Real-time Analysis of Situation Events Based on Hierarchical Events FusionThe definition of semantic event is given. Its three characteristics are analyzed. (1) The event is the spatio-temporal object; (2) The event is related with the dynamic environment; (3) The event has hierarchical structure. Hierarchical dynamic Bayesian network based on hierarchical events fusion is modeled for situation event analysis, and a real-time recognition method based on RBPF method is proposed. Based on the hierarchical features for the event and these relations among events at different levels, situation event is decomposed into a sequence of sub-events at different levels. The corresponding RBPF method are constructed for the inference of the posterior probability of each node in a hierarchical dynamic Bayesian network in order to recognize situation events in real time. Simulation experiments results show that this method can analyze situation event in real time and achieve the better recognition precision and the less computation time than the PF method.In a word, it can be drawn that the achievements and innovations summarized above effectively solve these key problems in the field of intelligent visual surveillances, and have broad application perspective and potential economic value. This dissertation is supported by National Natural Science Foundation of China under Grant 90304018, 60672137, the Doctoral Program Foundation of Ministry of Education of China under Grant 20060497015 and the Natural Science Foundation of Hubei Province (No. 2004 ABA043).更多还原