By Topic

Tracking by Affine Kernel Transformations Using Color and Boundary Cues

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Leichter, I. ; Comput. Sci. Dept., Technion - Israel Inst. of Technol., Haifa ; Lindenbaum, M. ; Rivlin, E.

Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in location and in scale) such that a function of the aggregate is optimized. We propose a kernel-based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary. The tracker estimates the best affinity of a spatially aligned pair of kernels, one of which is color-related and the other of which is object boundary-related. In a sense, this work extends previous kernel-based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled. These two extensions make for more precise target localization. A more accurately localized target also facilitates safer updating of its reference color model, further enhancing the tracker's robustness. The improved tracking is demonstrated for several challenging image sequences.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:31 ,  Issue: 1 )