A Dataset and Evaluation Framework for Deep Learning Based Video Stabilization Systems 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.

Deep Iterative Frame Interpolation for Fullframe Video Stabilization. Video stabilization is a fundamental and important technique for higher quality videos. Prior works have extensively explored video stabilization, but most of them involve cropping of the frame boundaries and introduce moderate levels of distortion. We present a novel deep approach to video stabilization which can generate video frames without cropping and low distortion. The proposed framework utilizes frame interpolation techniques to generate in between frames, leading to reduced interframe jitter. Once applied in an iterative fashion, the stabilization effect becomes stronger. A major advantage is that our framework is endtoend trainable in an unsupervised manner. In addition, our method is able to run in near realtime (15 fps). To the best of our knowledge, this is the first work to propose an unsupervised deep approach to fullframe video stabilization. We show the advantages of our method through quantitative and qualitative evaluations comparing to the stateoftheart methods.