Abstract

This article describes our ongoing research on real-time digital video stabilization using MEMS-sensors. The authors propose to use the described method for stabilizing the video that is transmitted to the mobile robot operator who controls the vehicle remotely, as well as increasing the precision of video-based navigation for subminiature autonomous models. The article describes the general mathematical models needed to implement the video stabilization module based on the MEMS sensors readings. These models includes the camera motion model, frame transformation model and rolling-shutter model. The existing approaches to stabilization using sensors data were analyzed and considered from the point of view of the application in a real-time mode. This article considers the main problems that came up during the experiments that were not resolved in the previous research papers. Such problems include: calibration of the camera and sensors, synchronization of the camera and sensors, increasing the accuracy of determining the camera position from sensors data. The authors offer possible solutions to these problems that would help improve quality of the work of existing algorithms, such as a system for parallel synchronized recording of video and sensor data based on the Android operating system. As the main result, the authors represent a framework for implementing video stabilization algorithms based on MEMS sensors readings.

Highlights

  • Modern cameras’ matrices allow to take high-quality pictures that are comparable to professional photographs

  • In the second section of the article, we review the existing approaches to digital video stabilization that utilize MEMS-sensors, analyze whether these algorithms are suitable for use in real time and list the mathematical models

  • Video stabilization process can be divided into 3 independent stages: estimating camera motion using MEMS sensors; calculating the desired camera motion in accordance to some logic; transforming the frame to match camera motion to the desired one

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Summary

Introduction

Modern cameras’ matrices allow to take high-quality pictures that are comparable to professional photographs. The main disadvantage of these algorithms [2], [3], [4], [5] is the amount of calculations needed to determine the camera motion This makes this method inapplicable for real-time video stabilization. You can estimate the camera motion during the recording by using the information from MEMS (MicroElectroMechanical Systems) motion sensors, including angular rate sensors (gyroscope), accelerometer and magnetometer This method requires less processing power to determine camera positioning and, is more energy-efficient, which makes it suitable for real-time video stabilization. In the second section of the article, we review the existing approaches to digital video stabilization that utilize MEMS-sensors, analyze whether these algorithms are suitable for use in real time and list the mathematical models. We list the main results of the ongoing research

Video stabilization
Mathematical models
Algorithm with Gaussian filter
Algorithm utilizing nonlinear filter
Frame transfomation
Camera calibrations and synchronization
Calibrating the unknown camera and sensor parameters
Synchronization of a camera and sensors
Current results
Conclusion
Full Text
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