In the current military operational environment, an increasingly large quantity of motion imagery data is being collected by both manned and unmanned airborne surveillance platforms. The Department of Defense has made a commitment to move the collection of this data into the digital domain to support more effective and efficient exploitation, targeting MPEG-2 as the decompression algorithm of choice. A major impediment to the exploitation and management of this motion imagery is the sheer volume of that data. With more information continually being provided to fewer people for exploitation, techniques need to be developed that will provide the analyst with manageable amounts of coherent video. Automatically segmenting the video into manageable duration clips is one way to achieve this. This paper describes an automatic, scene based, segmentation algorithm.
In this paper we describe a combination of Kalman filter with global motion estimation, between consecutive frames, implemented to improve target tracking in the presence of rapid motions of the camera encountered in human operated UAV based video surveillance systems. The global motion estimation allows to retain the localization of the tracked targets provided by the Kalman filter. The original target template is selected by the operator. SSD error measure is used to find the best match for the template in video frames.
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