Event cameras (EC) represent a paradigm shift and are emerging as valuable tools in the
automotive industry, particularly for in-cabin and out-of-cabin monitoring. These cameras …

The computer vision algorithms used currently in advanced driver assistance systems rely
on image-based RGB cameras, leading to a critical bandwidth–latency trade-off for …

Abstract We present Recurrent Vision Transformers (RVTs), a novel backbone for object
detection with event cameras. Event cameras provide visual information with sub …

Today state-of-the-art deep neural networks that process event-camera data first convert a
temporal window of events into dense grid-like input representations. As such they exhibit …

While recent Transformer-based approaches have shown impressive performances on
event-based object detection tasks their high computational costs still diminish the low …

Event cameras, offering extremely high temporal resolution and high dynamic range, have
brought a new perspective to addressing common object detection challenges (eg, motion …

Abstract Today, state-of-the-art deep neural networks that process events first convert them
into dense, grid-like input representations before using an off-the-shelf network. However …

DAVIS camera, streaming two complementary sensing modalities of asynchronous events
and frames, has gradually been used to address major object detection challenges (eg, fast …

Event cameras characterized by high temporal resolution high dynamic range low power
consumption and high pixel bandwidth offer unique capabilities for object detection in …

The combination of neuromorphic visual sensors and spiking neural network offers a high
efficient bio-inspired solution to real-world applications. However, processing event-based …