A Dataset and Evaluation Framework for Deep Learning Based Video Stabilization Systems
Maria Silvia Ito, Ebroul Izquierdo · 2019
Although traditional methods for video stabilization are time consuming and prone to failure, more promising Deep Learning based solutions have not been thoroughly studied yet. This is mostly due to the lack of suitable training and testing datasets. To address this problem, this paper introduces a comprehensive dataset for training and assessing techniques for video stabilization. It consists of many shaky video sequences, their stable videos and the respective motion parameters that map each frame of the stable video into the corresponding frame in the unstable video. An important aspect of the dataset is the availability of motion parameters. This critical feature enables better assessment of any video stabilization technique, since it allows for additional comparison of the estimated motion parameters with the provided Ground Truth motion parameters. Using this dataset, an evaluation framework for video stabilization technology is also introduced. To demonstrate the practical use of the introduced dataset and the evaluation framework, we compare the performance of two state of the art techniques for video stabilization. The results of this extensive evaluation are presented and both the database and the evaluation framework are also provided in this paper.