2D instantaneous frequency-based method for motion estimation using total variation

We present a first approach to a new method to compute the motion estimation in digital videos using the two-dimensional instantaneous frequency information computed using amplitude-modulation frequency-modulation (AM-FM) methods. The optical flow vectors are computed using an iteratively reweighted norm for total variation (IRN-TV) algorithm. We compare the proposed method using synthetic videos versus a previous three-dimensional AM-FM based method and available motion estimation methods such as a phase-based, Horn-Schunck and the Lucas-Kanade methods. The results are promising producing a full density estimation with more accurate results than the other methods.

I. Introduction

There is strong interest in the development of perception-based motion estimation methods that are based on multiscale amplitude-modulation frequency-modulation (AM-FM) methods [1]. The standard use of perception-based methods for motion estimation methods rely on the use of 3D Gabor filter banks that require a large number of video frames as discussed in [1]. In this paper, we present a new approach that uses a multi-scale AM-FM method to estimate optical flow motion using the instantaneous frequency information from two video frames only.