Video shutter angle estimation using optical flow and linear blur
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Abstract (arXiv)
We present a method for estimating the shutter angle, a.k.a. exposure fraction - the ratio of the exposure time and the reciprocal of frame rate - of videoclips containing motion. The approach exploits the relation of the exposure fraction, optical flow, and linear motion blur. Robustness is achieved by selecting image patches where both the optical flow and blur estimates are reliable, checking their consistency. The method was evaluated on the publicly available Beam-Splitter Dataset with a range of exposure fractions from 0.015 to 0.36. The best achieved mean absolute error of estimates was 0.039. We successfully test the suitability of the method for a forensic application of detection of video tampering by frame removal or insertion
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 17 Mar 2023.
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6 of 14 references.
References · 6
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2016Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurringcited by 722
- 2020Real-Time Intermediate Flow Estimation for Video Frame Interpolationcited by 211
- 2015Learning a Convolutional Neural Network for Non-uniform Motion Blur Removalcited by 148
- 2016From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blurcited by 81
- 2020Exposure Trajectory Recovery from Motion Blurcited by 31
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