文件名称:A-versatile-object-tracking-algorithm-combining-P
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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 descr iption, and
the Particle Filter, using a global descr iption. 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 descr iption, and
the Particle Filter, using a global descr iption. 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.
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 descr iption, and
the Particle Filter, using a global descr iption. 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 descr iption, and
the Particle Filter, using a global descr iption. 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.
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A versatile object tracking algorithm combining Particle Filter and generalised hough transform.pdf
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