Deep Event-based Object Detection in Autonomous Driving: A Survey

Fuente: arXiv
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Hauptverfasser: Zhou, Bingquan, Jiang, Jie
Format: Preprint
Veröffentlicht: 2024
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author Zhou, Bingquan
Jiang, Jie
author_facet Zhou, Bingquan
Jiang, Jie
contents Object detection plays a critical role in autonomous driving, where accurately and efficiently detecting objects in fast-moving scenes is crucial. Traditional frame-based cameras face challenges in balancing latency and bandwidth, necessitating the need for innovative solutions. Event cameras have emerged as promising sensors for autonomous driving due to their low latency, high dynamic range, and low power consumption. However, effectively utilizing the asynchronous and sparse event data presents challenges, particularly in maintaining low latency and lightweight architectures for object detection. This paper provides an overview of object detection using event data in autonomous driving, showcasing the competitive benefits of event cameras.
format Preprint
id arxiv_https___arxiv_org_abs_2405_03995
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Deep Event-based Object Detection in Autonomous Driving: A Survey
Zhou, Bingquan
Jiang, Jie
Computer Vision and Pattern Recognition
Object detection plays a critical role in autonomous driving, where accurately and efficiently detecting objects in fast-moving scenes is crucial. Traditional frame-based cameras face challenges in balancing latency and bandwidth, necessitating the need for innovative solutions. Event cameras have emerged as promising sensors for autonomous driving due to their low latency, high dynamic range, and low power consumption. However, effectively utilizing the asynchronous and sparse event data presents challenges, particularly in maintaining low latency and lightweight architectures for object detection. This paper provides an overview of object detection using event data in autonomous driving, showcasing the competitive benefits of event cameras.
title Deep Event-based Object Detection in Autonomous Driving: A Survey
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2405.03995