ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy

被引：1

作者：

Lee, JunKyu ^{[1
]}

Varghese, Blesson ^{[2
]}

Vandierendonck, Hans ^{[1
]}

机构：

[1] Queens Univ Belfast, Belfast, Antrim, North Ireland

[2] Univ St Andrews, St Andrews, Fife, Scotland

来源：

2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) | 2023年

基金：

英国工程与自然科学研究理事会;

关键词：

D O I：

10.1109/WACV56688.2023.00634

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.

引用

页码：6394 / 6403

页数：10

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