Motion detection with pyramid structure of background model for intelligent surveillance systems
Engineering Applications of Artificial Intelligence
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This paper investigates airborne helicopter video for estimating traffic parameters. Roll, pitch, and yaw of the helicopter make the video unstable, difficult to view, and the derived parameters less accurate. To correct this, a frame-by-frame video-registration technique using a feature tracker to automatically determine control-point correspondences is proposed. This converts the spatio-temporal video into temporal information, thereby correcting for airborne platform motion and attitude errors. The registration is robust, with the residual jitter being less than a few pixels over hundreds of frames. A simple vehicle-detection scheme identifies vehicle locations in the video, which are then tracked by the feature tracker, enabling us to estimate average velocity, instantaneous velocity, and other parameters automatically to within 10% of manual measurements. The entire process of registration, detection, tracking, and estimation takes only a few seconds for each frame. A prototype multimedia geographic information system (GIS) is created as a visualization tool for viewing the registered video, other airborne or satellite imagery, and data pertaining to georeferenced locations within a base map.