Probabilistic Object Tracking Using Multiple Features

We present a generic tracker which can handle a variety of different objects. For this purpose, groups of low-level features like interest points, edges, homogeneous and textured regions, are combined on a flexible and opportunistic basis. They sufficiently characterize an object and allow robust tracking as they are complementary sources of information which describe both the shape and the appearance of an object. These low-level features are integrated into a particle filter framework as this has proven very successful for non-linear and non-Gaussian estimation problems. In this paper we concentrate on rigid objects under affine transformations. Results on real-world scenes demonstrate the performance of the proposed tracker.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: David Serby, Esther Koller-Meier, Luc J. Van Gool
• Keywords: Computer Vision, ICPR 2004, Low-level Features, Non-Gaussian Estimation Problems, Particle Filter Framework