本文为印度Rourkela国立技术研究院（作者：Rupesh Kumar Rout）的硕士论文，共75页。

目标检测与跟踪在许多计算机视觉应用中都是一项重要而富有挑战性的任务，如监视、车辆导航、自主机器人导航等。视频动态环境下的监控，特别是对人和车辆的监控，是当前计算机视觉领域具有挑战性的研究课题之一。它是打击恐怖主义、犯罪、公共安全和有效管理交通的关键技术。本文的工作涉及复杂环境下高效视频监控系统的设计。在视频监控中，运动目标的检测对于目标检测、跟踪和具体行为理解具有重要意义。视频流中运动目标的检测是信息处理的第一步，背景减法是前景分割的常用方法。

在本论文中，我们模拟了不同的背景减除方法，以克服光照变化、背景杂波和阴影等问题。人体各部位的检测与跟踪是理解人体活动的重要环节。近年来，随着机场、地下车站、群体性事件等公共场所对智能化、自动化安全监控系统的需求日益增长，智能化、自动化的安全监控系统成为一个活跃的研究领域。在这种情况下，固定前景区域的跟踪是基于对被遗弃或被盗物体或停放车辆的跟踪监视系统的最关键的要求之一。基于目标跟踪的技术是检测静止前景对象的最常用方法，因为当摄像机静止且环境光照逐渐变化时，这种技术工作得相当好，而且它们也是将前景对象从当前帧中分离出来的最常用方法。监控网络通常由多个人监控，查看多个监视器显示的摄像头信息。当某个事件发生时，人类操作员很难有效地检测到它们。最近计算机视觉的研究已经解决了一些自动处理这些数据的方法，以辅助人类操作员的工作。

Object detection and tracking are importantand challenging tasks in many computer vision applications such assurveillance, vehicle navigation, and autonomous robot navigation.Videosurveillance in a dynamic environment, especially for humans and vehicles, isone of the current challenging research topics in computer vision.

It is a key technology to fight againstterrorism, crime, public safety and for efficient management of traffic. Thework involves designing of the efficient video surveillance system in complexenvironments. In video surveillance, detection of moving objects from a video isimportant for object detection, target tracking, and behavior understanding.Detection of moving objects in video streams is the first relevant step ofinformation and background subtraction is a very popular approach for foregroundsegmentation.

In this thesis, we have simulated differentbackground subtraction methods to overcome the problem of illuminationvariation, background clutter and shadows. Detecting and tracking of human bodyparts is important in understanding human activities. Intelligent and automatedsecurity surveillance systems have become an active research area in recenttime due to an increasing demand for such systems in public areas such asairports, underground stations and mass events. In this context, tracking ofstationary foreground regions is one of the most critical requirements forsurveillance systems based on the tracking of abandoned or stolen objects orparked vehicles. Object tracking based techniques is the most popular choice todetect stationary foreground objects because they work reasonably well when thecamera is stationary and the change in ambient lighting is gradual, and theyalso represent the most popular choice to separate foreground objects from thecurrent frame. Surveillance networks are typically monitored by a few people,viewing several monitors displaying the camera feeds. It is very difficult fora human operator to effectively detect events as they happen. Recently computervision research has to address ways to automatically some of this data, toassist human operators.

1. 引言

2. 项目背景

3. 文献回顾

4. 结论

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