Estimation of missing video frames using Kalman filter

Abstract

In recent years, there has been a lot of interest in the estimation of missing frames in video sequences because of its usage in several critical applications such as medical imaging, manufacturing, security systems, etc. The present study employs the Kalman filtering approach to suggest a technique for estimating missing frames within a video sequence and forecasting forthcoming frames. The Kalman filter model proposed in this study employs a pixel-by-pixel approach to leverage information from both preceding and subsequent video frames for the purpose of predicting the missing frame. The experimental findings demonstrate the effectiveness of the proposed technique. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.