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Tracking the Invisible: Learning Where the Object Might be

11 years 8 months ago
Tracking the Invisible: Learning Where the Object Might be
Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a method to learn supporters which are, be it only temporally, useful for determining the position of the object of interest. Our approach exploits the General Hough Transform strategy. It couples the supporters with the target and naturally distinguishes between strongly and weakly coupled motions. By this, the position of an object can be estimated even when it is not seen directly (e.g., fully occluded or outside of the image region) or when it changes its appearance quickly and significantly. Experiments show substantial improvements in model-free tracking as well as in the tracking of "virtual" points, e.g., in medical applications.
Helmut Grabner, Jiri Matas, Philippe Cattin, Luc V
Added 01 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Helmut Grabner, Jiri Matas, Philippe Cattin, Luc Van Gool
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