Removing outliers from the Lucas-Kanade method with a weighted median filter
Master thesis
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http://hdl.handle.net/11250/299147Utgivelsesdato
2015-06-15Metadata
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Sammendrag
The definition of optical flow is stated as a brightness pattern of apparent motion of
objects, through surfaces and edges in a visual scene. This technique is used in motion
detection and segmentation, video compression and robot navigation.
The Lucas-Kanade method uses information from the image structure to compose a gradient based solution to estimate velocities, also known as movement of X- and Y-direction in a scene. The goal is to obtain an accurate pixel motion from an image sequence
The objective of this thesis is to implement a post processing step with a weighted median
lter to a well known optical flow method; the Lucas-Kanade. The purpose is to use the
weighted median lter to remove outliers, vectors that are lost due to illumination changes
and partial occlusions.
The median filer will replace velocities that are under represented in neighbourhoods. A
moving object will have corners not just edges, and these vectors have to be preserved.
A weighted median filter is introduced to ensure that the under represented vectors is
preserved. Error is measured through angular and endpoint error, describing accuracy of
the vector field.
The iterative and hierarchical LK method have been studied. The iterative estimation
struggles less with single error. Because of this the weighted median filter did not improve
the iterative LK-method. The hierarchical estimation is improved by the weighted median
and reduced the average error of both angular and endpoint error.
Beskrivelse
Master's thesis in Automation and signal processing