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Updated: Oct 18, 2025

Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light

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Sub-Frame Evaluation of Frame Synchronization for Camera Network Using Linearly Oscillating Light Spot.

Hyuno Kim¹, Masatoshi Ishikawa¹

¹Data Science Research Division, Information Technology Center, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This study introduces a new method for camera network frame synchronization using light spot visuals. The technique achieves sub-millisecond accuracy, improving multi-camera data fusion.

Keywords:

camera network computer vision frame synchronization linear regression

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Area of Science:

Computer Vision
Optical Measurement
Network Synchronization

Background:

Accurate data fusion from multiple cameras requires precise frame synchronization.
Existing methods often rely on external instruments or luminescence, limiting applicability.

Purpose of the Study:

To develop a novel method for evaluating camera network frame synchronization accuracy.
To utilize inherent visual information from oscillating light spots for synchronization estimation.

Main Methods:

The proposed method analyzes images of a linearly oscillating light spot captured by the camera network.
Synchronization accuracy is estimated using the visual data, avoiding external devices.
The novel approach is benchmarked against conventional evaluation techniques.

Main Results:

The experimental results demonstrate the feasibility of the proposed method.
The estimation accuracy for frame synchronization was achieved in the sub-millisecond order.
The method effectively uses inherent visual cues for synchronization evaluation.

Conclusions:

The novel method provides a feasible and accurate approach to evaluate camera network frame synchronization.
Utilizing oscillating light spot visuals offers an alternative to traditional synchronization evaluation methods.
The sub-millisecond accuracy achieved has significant implications for precise visual data fusion.