Video Stabilisation Based on Spatial Transformer Networks

User-Generated Content is normally recorded with mobile phones by non-professionals, which leads to a low viewing experience due to artifacts such as jitter and blur. Other jittery videos are those recorded with mounted cameras or moving platforms. In these scenarios, Digital Video Stabilization (DVS) has been utilized, to create high quality, professional level videos. In the industry and academia, there are a number of traditional and Deep Learning (DL)-based DVS systems, however both approaches have limitations: the former struggles to extract and track features in a number of scenarios, and the latter struggles with camera path smoothing, a hard problem to define in this context. On the other hand, traditional methods have shown good performance in smoothing camera path whereas DL methods are effective in feature extraction, tracking, and motion parameter estimation. Hence, to the best of our knowledge the available DVS systems struggle to stabilize videos in a wide variety of scenarios, especially with high motion and certain scene content, such as textureless areas, dark scenes, close object, lack of depth, amongst others. Another challenge faced by current DVS implementations is the resulting artifacts that such systems add to the stabilized videos, degrading the viewing experience. These artifacts are mainly distortion, blur, zoom, and ghosting effects. In this thesis, we utilize the strengths of Deep Learning and traditional methods for video stabilization. Our approach is robust to a wide variety of scene content and camera motion, and avoids adding artifacts to the stabilized video. First, we provide a dataset and evaluation framework for Deep Learning-based DVS. Then, we present our image alignment module, which contains a Spatial Transformer Network (STN). Next, we leverage this module to propose a homography-based video stabilization system. Aiming at avoiding blur and distortion caused by homographies, our next proposal is a translation-based video stabilization method, which contains Exponential Weighted Moving Averages (EWMAs) to smooth the camera path. Finally, instead of using EWMAs, we study the utilization of filters in our approach. In this case, we compare a number of filters and choose the filters with best performance. Since the quality of experience of a viewer does not only consist of video stability, but also of blur and distortion, we consider it is a good trade off to allow some jitter left on the video while avoiding adding distortion and blur. In all three cases, we show that this approach pays off, since our systems ourperform the state-of-the-art proposals