A Comparative Study of Model-based and Learning-based Optical Flow Estimation methods with Event Cameras
D. Dinucu-Jianu (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 – Graduation committee member (TU Delft - Embedded Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Optical flow estimation with event cameras encompasses two primary algorithm classes: model-based and learning-based methods. Model-based approaches, do not require any training data while learning-based approaches utilize datasets of events to train neural networks. To effectively apply these algorithms, it's essential to understand their respective strengths and weaknesses.
This study compares model-based and learning-based optical flow estimation methods using event cameras, aiming to provide guidance for real-world applications. We evaluated these methods on the MVSEC and DSEC datasets, focusing on their accuracy and runtime. Our findings indicate that model-based methods excel on the MVSEC dataset, characterized by small motions, while learning-based approaches perform better on the more dynamic DSEC dataset. To investigate potential overfitting of learning-based methods to DSEC, we retrained the IDNet and TMA models on the BlinkFlow dataset. The retrained models demonstrated competitive accuracy, surpassing model-based methods which indicates that learning-based models perform better on datasets like DSEC even when not able to overfit. Finally, our analysis on runtime showed that model-based methods achieve real-time performance on CPUs and learning-based methods require a GPU to run in real-time.