Unsupervised optical flow estimation of event cameras
The influence of training sets on model performance
M. van den Berg (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Hesam Araghi – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Nergis Tomen – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
G. Lan – Mentor (TU Delft - Embedded Systems)
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Abstract
Event cameras are cameras that capture events asynchronously based on changes in lighting. They offer multiple benifits, but pose challenges in computer vision due to their asynchronous nature and hard to capture ground truth values to compare against. This paper shows the effects training of a state of the art unsupervised learning algorithm Taming Contrast Maximisation for predicting optical flow on a new dataset BlinkFlow which promises improvements in performance of supervised algorithms. This paper aims to see if these improved performances also happen for unsupervised models. Results of this research were inconclusive for the effectiveness of training unsupervised models, but it was shown that pretrained models on DSEC and MVSEC datasets did not perform well on this new dataset.