Details

Autor(en) / Beteiligte

• Zivkovic, Zoran
• Cemgil, Ali Taylan
• Kröse, Ben

Titel

Approximate Bayesian methods for kernel-based object tracking

Ist Teil von

• Computer vision and image understanding, 2009-06, Vol.113 (6), p.743-749

Ort / Verlag

Amsterdam: Elsevier Inc

Erscheinungsjahr

2009

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• A framework for real-time tracking of complex non-rigid objects is presented. The object shape is approximated by an ellipse and its appearance by histogram based features derived from local image properties. An efficient search procedure is used to find the image region with a histogram most similar to the histogram of the tracked object. The procedure is a natural extension of the mean-shift procedure with Gaussian kernel which allows handling the scale and orientation changes of the object. The presented procedure is integrated into a set of Bayesian filtering schemes. We compare the regular and mixture Kalman filter and other sequential importance sampling (particle filtering) techniques.