An Experimental Study of Image Components and Data Metrics for Illumination-Robust Variational Optical Flow

Illumination-robust optical flow algorithms are needed in numerous machine vision applications such as vision-based intelligent vehicles, surveillance and traffic monitoring. Recently, we have proposed an implicit nonlinear scheme for variational optical flow that assumes no particular analytical form of energy functional and can accommodate various image components and data metrics. Using test data with brightness and colour illumination changes, we study different features and metrics and demonstrate that cross-correlation is superior to the L₁ metric for all combinations of the features.