Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks
Tracking the invisible: Learning where the object might be
Book title (Conference Proceedings)
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Place of Conference
San Francisco, CA, USA
Publisher
IEEE
Pages
1285-1292
Abstract
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.
