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

提出了建立一个没有重叠视场的平移-倾斜-变焦监视摄像机网络的创新解决方案，该网络可以智能地校准和定位自身以及彼此之间的关系。目前，这类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。除了由中心的人工操作员提供的以外，摄像机之间通常不会进行协调。现有的监控研究主要集中在跟踪、监控和识别等经典的、研究较多的问题上。很少有人关注网络中摄像头的实际协调。对监测系统的一个重要方面缺乏重视，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极大地提高技术水平。摄像机的自标定将使用地平面的消失线和通过在多帧中跟踪人而获得的垂直消失点。通过考虑视频数据的时空体积中的不变轨迹，将准确测量受平移、倾斜或变焦影响的相机的自我运动。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临时重叠的视场，则将利用场景中常见的点，如果不可能重叠，则使用场景中的测量点。该项目的主要成果将是(1)一套创新的算法，可以改变目前的广域监视方法，并使其朝着自动化操作更近一步，(2)这些算法在真实监视系统中的实际实施和操作评估。可能从这一努力中受益的监视问题包括目标跟踪、行动检测、活动识别以及被跟踪对象和感兴趣的人的识别。因此，其影响和影响涉及监控的所有应用领域，但更具体地说，是在广域监控中，例如在机场或校园内的监控活动。广域监视是一个具有挑战性的研究课题，因为它需要摄像机的精心协调。拟议的研究是与国土安全相关的领域的重要一步。参与该项目的研究生将从一个日益重要和国家优先的领域的研究活动中受益；此外，国际和平研究所正在准备一门研究生级别的课程，主题是“基于视觉的监视”。网址：http://cil.eecs.ucf.edu/

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/