Enhancing Video Stability with Object-Centric Stabilization

Video stabilization is essential for producing high-quality videos by eliminating unwanted camera motion, resulting in smooth, steady footage that enhances visual coherence. However, existing methods often pose several challenges such as the difficulties in accurately tracking specific objects, and high computational demands. These limitations hinder the ability to achieve optimal stabilization results and limit the practical applicability of video stabilization techniques in real-world scenarios. To address these challenges, we introduce a novel end-to-end method for object-centric video stabilization in the presence of multiple independent objects. Our approach introduces two algorithms: 1) Object-Centric Video Stabilization (OCViS), which facilitates effective detection and tracking of specific objects within each frame, followed by stabilization with respect to the object of interest, and 2) Optical Flow-based Transformation Accumulation and Smoothing (OFTrAnS) to remove inherent shakiness and maintain consistent stabilization, even with multiple independent objects. Most importantly, our algorithms achieve these results without relying on deep neural networks or ground-truth data, providing a more efficient and accessible solution for enhancing video quality on mobile devices and handheld cameras. Our experiments demonstrate that the proposed approach significantly outperforms a strong baseline by a large margin emphasizing the efficacy of our method.