Frame Rate Impact on Flicker-Based Tracking Accuracy: A Comparative Study Using ROI Hue Metrics and YOLO Detection

Reliable tracking of high-speed motion is essential in sports analytics, where frame drops or motion blur can compromise accuracy and decision-making. This paper presents a modular, dual-pipeline architecture designed to evaluate the impact of frame rate on visual signal fidelity and object detection consistency. The proposed framework combines signal-level hue analysis and YOLOv12-based object detection into a unified system, enabling both low-level temporal signal assessment and high-level detection stability evaluation. LED flicker at four intervals ($17 \mathrm{~ms}, 25 \mathrm{~ms}, 30 \mathrm{~ms}$, and 50 ms) is used solely as a controlled temporal reference for evaluation, with sequences recorded at $25 \mathrm{FPS}, 30 \mathrm{FPS}$, and 60 FPS.Here we used the Flickerbased evaluation method to investigate how different video frame rates affect motion clarity and tracking reliability, Using both pixel-level signal analysis and object-level post-detection evaluation/ Experimental results demonstrate the architecture’s capability to quantify the relationship between frame rate, perceptual quality metrics (PQD-FRS, FRQM, etc.), and realtime detection reliability. Experimental findings show that flicker tracking error decreased by approximately 44% when increasing frame rate from 25FPS to 60FPS, while ROI-based $\boldsymbol{\triangle}$ Hue clarity improved by more than 100%, demonstrating stronger temporal fidelity and detection consistency at higher sampling rates. This contribution offers a reproducible, extensible benchmark for optimizing vision systems in sports, surveillance, and other fastaction environments, bridging engineered detection pipelines with human perceptual considerations.