2011 | OriginalPaper | Buchkapitel
Real-Time Estimation of Optical Flow Based on Optimized Haar Wavelet Features
verfasst von : Jan Salmen, Lukas Caup, Christian Igel
Erschienen in: Evolutionary Multi-Criterion Optimization
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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 real-time 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.