A Differentiable Recurrent Surface for Asynchronous Event-Based Data

• URL: http://arxiv.org/abs/2001.03455v2
• Date: Fri, 31 Jul 2020 08:56:34 GMT
• Title: A Differentiable Recurrent Surface for Asynchronous Event-Based Data
• Authors: Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci
• Abstract summary: We propose Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells that efficiently process events and learn end-to-end task-dependent event-surfaces. Compared to existing reconstruction approaches, our learned event-surface shows good flexibility and on optical flow estimation. It improves the state-of-the-art of event-based object classification on the N-Cars dataset.
• Score: 19.605628378366667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse representation of the scene. Therefore, to apply standard computer vision algorithms, events need to be integrated into a frame or event-surface. This is usually attained through hand-crafted grids that reconstruct the frame using ad-hoc heuristics. In this paper, we propose Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells that efficiently process events and learn end-to-end task-dependent event-surfaces. Compared to existing reconstruction approaches, our learned event-surface shows good flexibility and expressiveness on optical flow estimation on the MVSEC benchmark and it improves the state-of-the-art of event-based object classification on the N-Cars dataset.

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