Portal de Periódicos da CAPES

Approximate Bayesian methods for kernel-based object tracking

2009; Elsevier BV; Volume: 113; Issue: 6 Linguagem: Inglês

10.1016/j.cviu.2008.12.008

1090-235X

Zoran Živković, Ali Taylan Cemgil, Ben Kröse,

Gaussian Processes and Bayesian Inference

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.