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[arXiv]
[bibtex]@InProceedings{Lee_2023_WACV, author = {Lee, JunKyu and Varghese, Blesson and Vandierendonck, Hans}, title = {ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6405-6414} }
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
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 contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.
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