Motion estimation and object tracking based on time-sequentially sampled imagery

Two new algorithms are presented for estimating optical flow fields. These algorithms are based on the spatiotemporal properties of the data obtained time-sequentially from the field of view (FOV). The first is a centroid-based algorithm which estimates the velocity from the distance traveled by the centroid of the time-sequential data during the observation period. This algorithm performs well considering its simplicity. Next, a Fourier-based algorithm is presented. When motion within a time-varying scene is constant speed translation, the spectral energy is confined to a plane in the frequency domain. It is shown how to estimate a set of points on this plane using the 1-D FFT of the weighted time-sequential data. An estimate of the velocity vector is obtained by finding the orientation of the plane which gives a least squares fit to this set