Classifying stable and unstable videos with deep convolutional networks

Abstract

Since the invention of cameras, video shooting has become a passion for human. However, the quality of videos recorded with devices such as handheld cameras, head cameras, and vehicle cameras may be low due to shaking, jittering and unwanted periodic movements. Although the issue of video stabilization has been studied for decades, there is no consensus on how to measure the performance of a video stabilization method. In many studies in the literature, different metrics have been used for comparison of different methods. In this study, deep convolutional neural networks are used as a decision maker for video stabilization. VGG networks with different number of layers are used to determine the stability status of the videos. It was observed that VGG networks showed a classification performance up to 96.537% using only two consecutive scenes. These results show that deep learning networks can be utilized as a metric for video stabilization.