目标检测
BitTorrent跟踪器
计算机科学
人工智能
软件部署
跟踪(教育)
推论
深度学习
GSM演进的增强数据速率
对象(语法)
计算机视觉
探测器
区间(图论)
实时计算
模式识别(心理学)
眼动
软件工程
组合数学
电信
数学
教育学
心理学
作者
Pedro Azevedo,Vítor Santos
出处
期刊:Lecture notes in networks and systems
日期:2022-11-19
卷期号:: 297-308
标识
DOI:10.1007/978-3-031-21065-5_25
摘要
AbstractOne of the essential tasks for Autonomous Driving and Driving Assistance systems is the detection and tracking of Vulnerable Road Users (VRU) and traffic objects. Many recent developments in this area have been leveraging Deep Learning techniques. However, these Deep Learning models require heavy computational power. For this reason, optimising software components coupled with adequate hardware choices is crucial in the development of a system that can infer in real-time. This paper proposes solutions for object detection and tracking in an Autonomous Driving scenario by comparing and exploring the applicability of different State-of-the-art object detectors trained on the BDD100K dataset, namely YOLOv5, Scaled-YOLOv4 and YOLOR. In addition, the paper explores the deployment of these algorithms on Edge Devices, more specifically, the NVIDIA Jetson AGX Xavier. Furthermore, it examines the use of the DeepStream technology for real-time inference by comparing different object trackers, such as NvDCF and DeepSORT, in the KITTI tracking dataset. The proposed solution considers a YOLOR-CSP architecture with a DeepSORT tracker running at 33.3 FPS with a detection interval of one and 17 FPS with an interval of one.KeywordsObject detectionMultiple object trackingEdge devicesAutonomous vehiclesYOLODeep learningJetson AGXADAS
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