Vehicle video stabilization algorithm based on grid motion statistics and adaptive Kalman filtering

Owing to the impact of vibration on the carrier of a vehicle-mounted camera, video is shaking, resulting in decreased or failed recognition accuracy based on visual-target detection. To solve this problem, a video stabilization algorithm based on grid motion statistics and an adaptive Kalman filter is proposed. Two important processes in video stabilization are motion estimation and motion smoothing. In the motion estimation stage, we adopt an erroneous matching removal algorithm that integrates grid motion statistics (GMS) to enhance the accuracy of motion estimation while reducing the matching time, further meeting the real-time and precision requirements of vehicle-mounted video stabilization. In the motion smoothing stage, we adaptively update the measurement noise covariance R in the adaptive Kalman filter based on the camera shake level, further improving the accuracy of motion smoothing under the condition of ensuring filter convergence. Finally, we compensate for the motion based on the relationship between the pre- and postsmooth motion trajectories, generating a stable video sequence. Experimental results demonstrate that the proposed algorithm exhibits good stability and effectiveness in vehicle-mounted video stabilization.