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

提出了构建无视场重叠的平移变焦监控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，除了中心的人工操作人员提供的协调。现有的监控研究主要集中在跟踪、监控和识别等经典问题上。很少有人关注网络中摄像机的实际协调。缺乏对监测系统的一个重要方面的关注为本项目将探讨的发现提供了机会。对这些问题提出的解决办法将大大提高技术水平。摄像机的自标定将使用地平面的消失线和多帧跟踪人得到的垂直消失点。摄像机在平移、倾斜或变焦的情况下的自我运动将通过考虑视频数据的时空体积中的不变轨迹来精确测量。相机之间的相对位置，只需要计算一次，将利用场景中常见的点，如果相机可以平移到临时重叠的视场，以及场景中的调查点，如果重叠是不可能的。该项目的主要成果将是：(1)一套创新算法，可以将当前的方法转变为广域监视，并使其更接近自动化操作；(2)这些算法在实际监视系统中的实际实施和操作评估。可以从这项工作中受益的监视问题是目标跟踪、行动检测、活动识别以及对跟踪对象和感兴趣的人的识别。因此，其含义和影响涉及监控的所有应用领域，但更具体地说，在广域监控中，例如在机场或校园的监控活动。广域监控是一个具有挑战性的研究课题，因为它需要精心协调的摄像机。拟议的研究是在国土安全相关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正在准备一门关于“基于视觉的监视”的研究生课程。URL: 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/