E-HANet:Event-based Hybrid Attention Network for Optical Flow Estimation

Optical flow estimation is an essential task in computer vision. Standard cameras are prone to blurred images or over-saturated regions under extreme conditions. The event camera is a novel vision sensor inspired by the biological retina. It has the advantages of high time resolution, low delay and high dynamic range. We propose a new event representation and a novel deep learning network E-HANet (Event-based Hybrid Attention Network) for event-based dense optical flow estimation. To take full advantage of the complementarity between positive and negative events, we introduce the stacked positive and negative event slices as input. The feature extractor based on the channel attention is able to model the features from different event slices and fuse them by weight. We then present the hybrid attention weighting module to globally aggregate motion features. Compared to the event-based state-of-the-art, our approach reduces the average end-point error by 3% on the MVSEC dataset and 20% on the DSEC-Flow dataset.