  Event-based cameras have recently drawn the attention of the Computer Vision
community thanks to their advantages in terms of high temporal resolution, low
power consumption and high dynamic range, compared to traditional frame-based
cameras. These properties make event-based cameras an ideal choice for
autonomous vehicles, robot navigation or UAV vision, among others. However, the
accuracy of event-based object classification algorithms, which is of crucial
importance for any reliable system working in real-world conditions, is still
far behind their frame-based counterparts. Two main reasons for this
performance gap are: 1. The lack of effective low-level representations and
architectures for event-based object classification and 2. The absence of large
real-world event-based datasets. In this paper we address both problems. First,
we introduce a novel event-based feature representation together with a new
machine learning architecture. Compared to previous approaches, we use local
memory units to efficiently leverage past temporal information and build a
robust event-based representation. Second, we release the first large
real-world event-based dataset for object classification. We compare our method
to the state-of-the-art with extensive experiments, showing better
classification performance and real-time computation.
