Modulation-Domain Particle Filter for Template Tracking

Particle filters provide a robust framework for non-linear and non-Gaussian estimation problems. In this paper we present a method to incorporate dominant modulation-domain (Amplitude Modulation-Frequency Modulation) features into particle filter based template tracking. The dominant AM-FM features capture the local texture structure of an image. Tracking is performed based on correlations of these AM-FM features. We propose an adaptive method that weights these correlations based on the significance of the features for tracking. Tire performance of the tracker is demonstrated on a few standard test sequences.