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【正文】 d analysis of video surveillance data to alert security officers to a burglary in progress, or to a suspicious individual loitering in the parking lot, while options are still open for avoiding the crime. Keeping track of people, vehicles, and their interactions in an urban or battlefield environment is a difficult task. The role of VSAM video understanding technology in achieving this goal is to automatically “parse” people and vehicles from raw video, determine their geolocations, and insert them into dynamic scene visualization. We have developed robust routines for detecting and tracking moving objects. Detected objects are classified into semantic categories such as human, human group, car, and truck using shape and color analysis, and these labels are used to improve tracking using temporal consistency constraints. Further classification of human activity, such as walking and running, has also been achieved. Geolocations of labeled entities are determined from their image coordinates using either widebaseline stereo from two or more overlapping camera views, or intersection of viewing rays with a terrain model from monocular views. These puted locations feed into a higher level tracking module that tasks multiple sensors with variable pan, tilt and zoom to cooperatively and continuously track an object through the scene. All resulting object hypotheses from all sensors are transmitted as symbolic data packets back to a central operator control unit, where they are displayed on a graphical user interface to give a broad overview of scene activities. These technologies have been demonstrated through a series of yearly demos, using a testbed system developed on the urban campus of CMU. Detection of moving objects in video streams is known to be a significant, and difficult, research problem. Aside from the intrinsic usefulness of being able to s
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