Abstract: To address the challenge of accurately detecting vibration parameters in vibrating screening equipment, an in-situ measurement system based on machine vision was designed. A high-speed area-scan camera was employed for high-resolution image acquisition. The system's robustness under low-light and low-contrast conditions was enhanced through a ring light source, infrared filter, and auto-focus module. Camera calibration based on corner detection improved the real-time performance. A multi-scale image process enabled the feature marker extraction with sub-pixel localization at a low computational complexity. A single-cycle curve fitting method utilizing spline interpolation was proposed for real-time vibration parameter acquisition. Within a 0.8 m measurement range, the system achieved a resolution lower than 0.12 mm/pixel. The calibration, feature extraction, and curve fitting algorithms demonstrated over 50% improvement in computational efficiency compared to conventional methods. The proposed spline-based single-cycle fitting reduced peak-to-peak value and frequency errors from 0.09 mm and 0.85 r/min (multi-cycle fitting) to 0.06 mm and 0.35 r/min, representing reductions of 33.33% and 58.82%, respectively. This meets tobacco industry standards for vibration detection instruments. The effectiveness of the proposed system was verified through in-situ parameter measurement. This system satisfies in-situ vibration monitoring requirements for vibrating screens used in cut tobacco structure and lamina size analysis.