Stochastic Modeling for Video Object Tracking and Online Learning: manifolds and particle filters

Sammanfattning: Classical visual object tracking techniques provide effective methods when parameters of the underlying process lie in a vector space. However, various parameter spaces commonly occurring in visual tracking violate this assumption. This thesis is an attempt to investigate robust visual object tracking and online learning methods for parameter spaces having vector or manifold structures. For vector spaces, two different methods are proposed for video tracking. The first builds upon anisotropic mean-shift tracker for appearance similarity and SIR particle filter for tracking of the bounding box. The anisotropic mean shift is derived for a partitioned rectangular bounding box and several partition prototypes with adaptive learning strategy of reference object distributions. The joint scheme maintains the merits of both methods, using a small number of particles (