Estimation of optical flow is required in many computer vision applications.
These applications often have to deal with strict time constraints. Therefore,
flow algorithms with both high accuracy and computational efficiency are
desirable. Accordingly, designing such a flow algorithm involves multi-objective
optimization. In this work, we build on a popular algorithm developed for realtime applications. It is originally based on the Census transform and benefits
from this encoding for table-based matching and tracking of interest points. We
propose to use the more universal Haar wavelet features instead of the Census
transform within the same framework. The resulting approach is more flexible,
in particular it allows for sub-pixel accuracy. For comparison with the original
method and another baseline algorithm, we considered both popular benchmark
datasets as well as a long synthetic video sequence. We employed evolutionary
multi-objective optimization to tune the algorithms. This allows to compare the
different approaches in a systematic and unbiased way. Our results show that
the overall performance of our method is significantly higher compared to the
reference implementation.