ROMA: run-time object detection To maximize real-time accuracy

JunKyu Lee*, Blesson Varghese, Hans Vandierendonck

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

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 con-tents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.
Original languageEnglish
Publication statusPublished - 28 Oct 2022
EventIEEE/CVF Winter Conference on Applications of Computer Vision - Waikoloa, United States
Duration: 3 Jan 20237 Jan 2023
https://wacv2023.thecvf.com
https://doi.org/10.1109/WACV56688.2023

Conference

ConferenceIEEE/CVF Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2023
Country/TerritoryUnited States
CityWaikoloa
Period3/01/237/01/23
Internet address

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  • ROMA: run-time object detection to maximize real-time accuracy

    Lee, J., Varghese, B. & Vandierendonck, H., 6 Feb 2023, Proceedings 2023 IEEE/CVF winter conference on applications of computer vision (WACV): 3-7 January 2023, Waikoloa, Hawaii. Berg, T. & Farrell, R. (eds.). Piscataway, NJ: IEEE, p. 6394-6403 10 p. 10030225. (2023 IEEE/CVF winter conference on applications of computer vision (WACV)).

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