Video stabilization algorithm for tunnel robots based on improved Kalman filter

Abstract

The application of tunnel robots in power tunnels is becoming more and more extensive. Due to various reasons, video jitter will inevitably occur during the inspection process, which will affect the real-time processing of subsequent images. In order to reduce the impact of video jitter and meet the requirements of real-time image processing, it is necessary to study fast video image stabilization methods. The traditional Kalman filtering algorithm has less computational cost, but it will cause a large estimation error when the system motion state changes. In this regard, the paper proposes an improved Kalman filtering algorithm, which changes the corresponding filter parameters by real-time estimation of a system in motion to reduce the estimation error. And this algorithm performs Harris corner extraction in the set feature dense area, and combines the PyrLK optical flow algorithm with the homography matrix to calculate the motion trajectory, which improves the calculation speed and accuracy of the algorithm. Experimental results show that this algorithm has better video stabilization effect and faster calculation speed compared with the traditional Kalman filtering algorithm, and can better meet the real-time image processing requirements in the inspection process.