Bayesian filtering and integral image for visual tracking

This paper describes contributions to two problems related tovisual tracking: control model design and observation process
design. We describe the use of kernel-based Bayesian filtering
for the tracking control procedure, and feature-based tracking
to improve the observation process of tracking. In the kernel-
based Bayesian filtering framework, the analytical represen-
tation of density functions by density interpolation and density
approximation for the likelihood and the posterior contributes
to efficient sampling. Feature-based tracking combines rec-
tangular features with edge oriented histogram so that the
combined features are robust to illumination changes, partial
occlusion, and clutter while capturing the spatial information
of the target. The use of integral image allows the features to
be efficiently evaluated. The effectiveness of both algorithms
are demonstrated by object tracking results on real videos.