Digital Video Stabilization using SIFT Feature Matching and Adaptive Fuzzy Filter

Context: Video stabilization techniques have gained popularity for their permit to obtain high quality video footage even in non-optimal conditions. There have been significant works done on video stabilization by developing different algorithms. Most of the stabilization software displays the missing image areas in stabilized video. In the last few years hand-held video cameras have continued to grow in popularity, allowing everyone to easily produce personal video footage. Furthermore, with online video sharing resources being used by a rapidly increasing number of users, a large proportion of such video footage is shared with wide audiences. Sadly such videos often suffer from poor quality as frame vibration in video makes human perception not comfortable. In this research an attempt has been made to propose a robust video stabilization algorithm that stabilizes the videos effectively. Objectives: The main objective of our thesis work is to perform effective motion estimation using SIFT features to calculate the inter frame motion, allowing to find Global Motion Vectors and optimal motion compensation is to be achieved using adaptive fuzzy filter by removing the unwanted shakiness and preserve the panning leading to stabilized video. Methods: In this study three types of research questions are used- Experimentation and Literature review. To accomplish the goal of this thesis, experimentation is carried out for performing video stabilization. Motion estimation is done using feature based motion estimation using SIFT and GMVs are calculated. The intentional motion is filtered using Adaptive fuzzy filter to preserve panning and Motion compensation is performed to wrap the video to its stabilized position. MOS implies the mean scores of the subjective tests performed according to the recommendations of ITU-R BT.500-13 and ITU-T P.910 to analyze the results of our stabilized videos. Results: As a part of results from our work, we have successfully stabilized the videos of different resolutions from experimentation. Performance of our algorithm is found better using MOS. Conclusions: Video Stabilization can be achieved successfully by using SIFT features with pre conditions defined for feature matching and attempts are made to improve the video stabilization process