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Abstract
In recent years, with the rapid development of computer vision technology, embedded
technology and communication technology, the intelligent video surveillance technology has been
more and more widely used in many fields of society. Moving object detection and tracking is not
only one of the core technology of intelligent video surveillance technology, but also the basis for
intelligent video surveillance applications, which currently is moving forward embedded
technology and communications and networks technology rapid development. This research have
very good prospects in scientific research and practical application, so based on embedded
intelligent video surveillance technology to attract the majority of researchers has become a
prominent research focus .
The thesis has finished a deep theoretic study and sufficient experimental works in moving
object detection and tracking in static background, and design improved algorithms. The main work
of this thesis is as follows:
On research of the moving object detection, the first analyze several the currently main three
algorithms of motion detection: optical flow method, background subtraction, inter-frame difference
method, in which focus on the advantages and disadvantages of the inter-frame difference and
background subtraction, improve both, and combine with the edge detecting operators, and evaluate
the performance of three edge detection operators objectively, and then design a motion object
detection algorithm based on background edge-characteristic difference combining five inter-frame
difference. The experimental results show that the algorithm can eliminate the shadow and inanition
and quickly extract a complete, accurate cont
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