An Event-based Feature Representation Method for Event Stream Classification using Deep Spiking Neural Networks

Event streams output by event cameras have low data redundancy and retain accurate temporal information in the form of Address Event Representation (AER) which are different from the outputs of traditional frame-based cameras. Spiking Neural Networks (SNNs) are considered an effective tool for handling event-based scenarios due to their inherent temporal properties. However, most existing SNNs directly convert an event stream to several static frames with temporal relationships by channel-wise accumulation of events. These serial frames lose temporal characteristics in some extent and potentially affect the capacity of the SNNs to learn and recognize event streams. In this work, we proposed a novel event-based feature descriptor called time interval correlation time-surface (TICTS) for SNNs and introduced this event-based feature extraction method into SNNs. The TICTS can capture more precise temporal correlation from event streams, thereby facilitating SNNs to learn temporal information more effectively. The experimental results show that SNNs with the proposed TICTS exhibit superior performance and increased stability across datasets with varying speeds. In addition, shallow network using TICTS can achieve competitive accuracy compared to deep networks, underscoring the effectiveness of TICTS in reducing the redundant network size of SNNs.