  Event-based cameras are bio-inspired sensors that capture brightness change
of every pixel in an asynchronous manner. Compared with frame-based sensors,
event cameras have microsecond-level latency and high dynamic range, hence
showing great potential for object detection under high-speed motion and poor
illumination conditions. Due to sparsity and asynchronism nature with event
streams, most of existing approaches resort to hand-crafted methods to convert
event data into 2D grid representation. However, they are sub-optimal in
aggregating information from event stream for object detection. In this work,
we propose to learn an event representation optimized for event-based object
detection. Specifically, event streams are divided into grids in the x-y-t
coordinates for both positive and negative polarity, producing a set of pillars
as 3D tensor representation. To fully exploit information with event streams to
detect objects, a dual-memory aggregation network (DMANet) is proposed to
leverage both long and short memory along event streams to aggregate effective
information for object detection. Long memory is encoded in the hidden state of
adaptive convLSTMs while short memory is modeled by computing spatial-temporal
correlation between event pillars at neighboring time intervals. Extensive
experiments on the recently released event-based automotive detection dataset
demonstrate the effectiveness of the proposed method.
