Event-based Temporally Dense Optical Flow Estimation with Sequential Learning

• URL: http://arxiv.org/abs/2210.01244v2
• Date: Thu, 12 Oct 2023 01:44:33 GMT
• Title: Event-based Temporally Dense Optical Flow Estimation with Sequential Learning
• Authors: Wachirawit Ponghiran, Chamika Mihiranga Liyanagedera and Kaushik Roy
• Abstract summary: Event cameras capture fast-moving objects without a motion blur. We show that a temporally dense flow estimation at 100Hz can be achieved by treating the flow estimation as a problem.
• Score: 11.026299772309796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras provide an advantage over traditional frame-based cameras when capturing fast-moving objects without a motion blur. They achieve this by recording changes in light intensity (known as events), thus allowing them to operate at a much higher frequency and making them suitable for capturing motions in a highly dynamic scene. Many recent studies have proposed methods to train neural networks (NNs) for predicting optical flow from events. However, they often rely on a spatio-temporal representation constructed from events over a fixed interval, such as 10Hz used in training on the DSEC dataset. This limitation restricts the flow prediction to the same interval (10Hz) whereas the fast speed of event cameras, which can operate up to 3kHz, has not been effectively utilized. In this work, we show that a temporally dense flow estimation at 100Hz can be achieved by treating the flow estimation as a sequential problem using two different variants of recurrent networks - Long-short term memory (LSTM) and spiking neural network (SNN). First, We utilize the NN model constructed similar to the popular EV-FlowNet but with LSTM layers to demonstrate the efficiency of our training method. The model not only produces 10x more frequent optical flow than the existing ones, but the estimated flows also have 13% lower errors than predictions from the baseline EV-FlowNet. Second, we construct an EV-FlowNet SNN but with leaky integrate and fire neurons to efficiently capture the temporal dynamics. We found that simple inherent recurrent dynamics of SNN lead to significant parameter reduction compared to the LSTM model. In addition, because of its event-driven computation, the spiking model is estimated to consume only 1.5% energy of the LSTM model, highlighting the efficiency of SNN in processing events and the potential for achieving temporally dense flow.

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