A method for prediction and estimation of large‐amplitude optical flows via extended Kalman filtering approach

Abstract

Purpose

This paper seeks to develop a reliable and computationally efficient method for estimating and predicting large‐amplitude optical flows via taking into consideration their coherence along the time dimension.

Design/methodology/approach

Although the differential‐based techniques for estimating optical flows have long been in wide use owing to the relative simplicity of their mathematical description, their applicability is known to be limited to the situations, when the optical flow has a relatively small norm. In order to extend such method to deal with large‐amplitude optical flows, it is proposed to model the optical flow as a composition of its time‐delayed version and a complementary optical flow. The former is used to predict the current optical flow and, subsequently, to warp forward the preceding image of the tracking sequence, while the latter accounts for the residual displacements that are estimated using Kalman filtering based on the “small norm” assumption.

Findings

The study shows that taking into consideration the temporal coherence of optical flows results in considerable improvement in the quality of their estimation in the case when the amplitude of the optical flow is relatively large and, hence, the “small norm” assumption is not applicable.

Research limitations/implications

In the present work, the algorithm is formulated under the assumption that the optical flow is affine. This assumption may be restrictive in practice. Consequently, an important direction to extend this work is to consider more general classes of optical flows.

Originality/value

The main contribution of the present study is the use of multigrid methods and a projection scheme to relate the state equation to the apparent image motion.

Citation

Michailovich, O. and Tannenbaum, A. (2006), "A method for prediction and estimation of large‐amplitude optical flows via extended Kalman filtering approach", Engineering Computations, Vol. 23 No. 5, pp. 503-514. https://doi.org/10.1108/02644400610671108