Abstract
The problem of detecting areas of motion in video sequences and estimating parameters such as speed, direction and dynamics is addressed in many applications of image processing such as video surveillance, object tracking, image stream compression or autonomous navigation systems. Real world computer vision highly depends on reliable, robust systems for recognition of motion cues to make accurate high-level decisions about its surroundings. In this paper, we present a simple, yet high performance low-level filter for motion detection and estimation in digitized video signals. The algorithm is based on constant characteristics of a common, 2-frame interlaced video signal, yet its applicability to generically acquired image sequences is shown as well. In general, our approach presents a computationally low-cost solution to motion estimation application and compares very well to existing approaches due to its robustness towards environmental changes. A simple application of motion parameter estimation based on a pedestrian surveillance application is illustrated.
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