SGER: Network of Surveillance Cameras with Active Zoom and Dynamic Topology

SGER：具有主动变焦和动态拓扑的监控摄像头网络

• 批准号: 0644280
• 负责人: Hassan Foroosh
• 金额: --
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644280&HistoricalAwards=false
• 关键词: SGER; Network; Surveillance; Cameras; Active

Research is proposed on innovative solutions for building a network of pan-tilt-zoom surveillance cameras without overlapping fields of view that can intelligently calibrate and localize themselves and in relation to each other. Currently, such systems typically consist of a central recording center, with hundreds of hours of tapes recorded daily. There is usually no coordination between cameras, other than what is provided by human operators at the center. Existing research in surveillance is primarily focused on classical and highly studied problems such as tracking, monitoring, and recognition. Little attention has been paid on the actual coordination of cameras in a network. This lack of attention to an important aspect of surveillance systems presents opportunities for discovery that will be explored in this project. The proposed solutions to these problems will significantly advance the state of the art. Self calibration of cameras will use the vanishing line of the ground plane and the vertical vanishing point obtained by tracking people over multiple frames. Ego-motion of cameras subject to pan, tilt or zoom will be accurately measured by considering invariant trajectories in space- time volumes of video data. Relative positions between cameras, which need to be computed only once, will make use of commonly seen points of the scene if cameras can be panned to have temporary overlaps of fields of view, and survey points in the scene if overlaps are not possible. The main outcomes of this project will be (1) a set of innovative algorithms that could transform the current approach to wide area surveillance, and move it one step closer towards automated operation, and (2) an actual implementation and operational evaluation of these algorithms with real surveillance systems.Surveillance issues that could benefit from this effort are target tracking, detection of actions, identification of activities, and recognition of tracked objects and people of interest. The implications and the impact are therefore across all application areas in surveillance, but more specifically in wide area surveillance, e.g. monitoring activities in an airport or on a campus. Wide area surveillance is a challenging research topic, since it requires orchestrated coordination of cameras.The proposed research is an important step ahead in an area related to homeland security. The graduate students involved in the project will benefit from research activities in an area of growing importance and national priority; in addition, the PI is preparing a graduate-level course on "vision-based surveillance".URL: http://cil.eecs.ucf.edu/

研究提出了创新的解决方案，用于建立一个没有重叠视野的云台变焦监控摄像机网络，可以智能地校准和定位自己和彼此之间的关系。目前，这种系统通常由一个中央录音中心组成，每天录制数百小时的磁带。 摄像机之间通常没有协调，除了由中心的人类操作员提供的协调。现有的监控研究主要集中在经典和高度研究的问题，如跟踪，监控和识别。很少有人关注网络中摄像机的实际协调。这种对监视系统的一个重要方面缺乏关注的情况，为本项目将探讨的发现提供了机会。针对这些问题提出的解决方案将显著推进现有技术的发展。摄像机的自校准将使用地平面的消失线和通过在多个帧上跟踪人获得的垂直消失点。通过考虑视频数据的空间-时间体积中的不变轨迹，将精确地测量受到平移、倾斜或缩放的相机的自我运动。相机之间的相对位置只需要计算一次，如果相机可以平移以具有视场的临时重叠，则将利用场景中常见的点，如果重叠是不可能的，则利用场景中的测量点。该项目的主要成果将是（1）一套创新的算法，可以改变目前的方法，以广域监视，并使其更接近自动化操作，和（2）实际实施和业务评估这些算法与真实的监视系统。监视问题，可以受益于这一努力的目标跟踪，检测的行动，识别的活动，以及识别所跟踪的对象和感兴趣的人。因此，其含义和影响遍及监控的所有应用领域，但更具体地说，是在广域监控中，例如监控机场或校园中的活动。广域监控是一个具有挑战性的研究课题，因为它需要协调摄像机。拟议的研究是在与国土安全有关的领域向前迈出的重要一步。参与该项目的研究生将受益于一个日益重要和国家优先领域的研究活动;此外，PI正在准备一个关于“基于视觉的监视”的研究生课程。http://cil.eecs.ucf.edu/