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Developer Documentation |
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Centre for Vision, Speech & Signal Processing |
Comments:
FeatureTrackC is responsible for performing temporal filtering for the
DERA Small Target Tracking project. The purpose is to invesigate the
history of feature measurements to determine whether the target is
interesting or not.
The algorithm has impact in 3 main ways:
- Each target to be considered must have been successfully tracked for
at least n consecutive frames, where n is the length of history specified
in the constructor.
- Linear models are fitted to region size, average and gradient contrast,
and compactness using the measured values in the history and their frame
index as ordinate value. Tests are then performed on each feature to
determine if the feature is behaving predictably by calculating the ratio
of the predicted feature value for the current frame to the actual value
for the current frame. The different features have to satisfy the following
restrictions:
0.6 < size ratio < 1.66
0.8 < average contrast ratio < 1.25
0.8 < gradient contrast ratio < 1.25
0.8 < compactness ratio < 1.25
- Very small stationary targets are eliminated by finding the average
motion of the target during the history. The target is rejected if the mean
motion magnitude is less than 0.3 pixels and the target is less than 4
pixels in size.
All targets that satisfy all of the above criteria are returned in a list
of RegBoundaryC objects.
Variables:
- UIntT _history;
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| ind | Index of types of intererest. |
Methods:
- FeatureTrackC(UIntT history)
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Constructor
| ind | Index of types of intererest. |
- DListC<RegBoundaryC> Filter(DListC<RegBoundaryC> & before) const
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Prunes list of regions according to their history
| ind | Index of types of intererest. |
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Programmer:Robert Crida, Documentation by CxxDoc: Tue Mar 20 10:49:27 2001
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