Hybrid Kalman Filter with Adaptive Motion Modelling for Video Stabilization

An innovative video stabilization method that merges Kalman filtering advantages with adaptive motion modeling for better video sequence stabilization results. The suggested method overcomes the drawbacks of conventional video stabilization techniques, which frequently depend on basic motion models or struggle to adjust to evolving camera movements. Our algorithm utilizes a blend of Kalman filter framework incorporating various motion models such as translation, rotation, and affine transformations. The dynamic motion modeling element evaluates the video content and camera movement in live feed, choosing the best motion model and fine-tuning its parameters to enhance stabilization efficiency. The hybrid Kalman filter predicts and updates the camera’s motion state by combining measurement updates with feedback from feature tracking and optical flow. The algorithm is able to effectively manage a variety of camera motions, from gentle and gradual to rapid and unpredictable, thanks to its adaptive model selection and parameter adaptation. Our method has been shown through experiments to effectively stabilize video sequences with complex motion patterns, surpassing current methods in stability and smoothness. The algorithm’s ability to adjust and resilience makes it appropriate for a range of uses such as live video streaming, sports broadcasting, and surveillance.