  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.
