Object tracking using AM-FM image features

Abstract

AM-FM models analyse an image in terms of amplitude (AM) and frequency modulated (FM) sinusoids. In this study, the authors present detection and tracking of single and multiple objects in video sequences using AM-FM features. The authors use the particle filtering framework for estimating the motion parameters. The single object tracking algorithm uses an affine motion model and a subspace-based appearance model. The multiple object tracking algorithm, which is a logical extension of single object tracking, can handle varying number of interacting objects. The performance of both single and multiple object tracking is illustrated on real-world videos.