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Description: Motion Tracking
=== === ===
This tarball contains all code required to run the tracking algorithm
on a sequence of images. Run the file run_tracker.m in Matlab and
follow the instructions. You will need to have a directory of
sequentially numbered images available. After entering the path and
file types the tracker will begin processing. Once the data window
appears the algorithm begins building a background model and attempts
to track objects. By clicking on any of the four subwindows you can
investigate the background representation (a Mixture of Gaussians) of
any pixel. The two windows that then appear display the mixture once
as a two-dimensional scatter plot (ignoring the blue colour
component), and once as a one-dimensional evolution of the red colour
component only. These plots make the internal processing visible and
should help determining suitable parameters to be set in
mixture_parameters.m.-Motion Tracking
===============
This tarball contains all code required to run the tracking algorithm
on a sequence of images. Run the file run_tracker.m in Matlab and
follow the instructions. You will need to have a directory of
sequentially numbered images available. After entering the path and
file types the tracker will begin processing. Once the data window
appears the algorithm begins building a background model and attempts
to track objects. By clicking on any of the four subwindows you can
investigate the background representation (a Mixture of Gaussians) of
any pixel. The two windows that then appear display the mixture once
as a two-dimensional scatter plot (ignoring the blue colour
component), and once as a one-dimensional evolution of the red colour
component only. These plots make the internal processing visible and
should help determining suitable parameters to be set in
mixture_parameters.m.
Platform: |
Size: 36511744 |
Author: gobsy |
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Description: red colour object tracker in real time in matlab
Platform: |
Size: 1024 |
Author: soumen patra |
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Description: This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and
the Particle Filter, using a global description. The object model is
updated by combining information a back-projection map
computed the Generalised Hough Transform, providing
for every pixel the degree to which it may belong to the
object, and the Particle Filter, providing a probability
density on the global object position. The proposed tracker
makes the most of the two algorithms, in terms of robustness to
appearance variation like scaling, rotation, non-rigid deformation
or illumination changes.-This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and
the Particle Filter, using a global description. The object model is
updated by combining information a back-projection map
computed the Generalised Hough Transform, providing
for every pixel the degree to which it may belong to the
object, and the Particle Filter, providing a probability
density on the global object position. The proposed tracker
makes the most of the two algorithms, in terms of robustness to
appearance variation like scaling, rotation, non-rigid deformation
or illumination changes.
Platform: |
Size: 2140160 |
Author: SALEH |
Hits: