Video shutter angle estimation using optical flow and linear blur
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
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Flow EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video
Rolling shutter (RS) images can be viewed as the result of the row-wise combination of global shutter (GS) images captured by a virtual moving GS camera over the period of camera readout time. The RS effect brings tr…
Optical Flow EstimationSuper-ResolutionRolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video
A single rolling-shutter (RS) image may be viewed as a row-wise combination of a sequence of global-shutter (GS) images captured by a (virtual) moving GS camera within the exposure duration. Although RS cameras are widel…
Optical Flow EstimationSuper-ResolutionRolling-Shutter-Aware Differential SfM and Image Rectification
In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two consecutive frames despite of Rolling Shutter (RS) artifacts. In particular, we show that u…
3D ReconstructionOptical Flow EstimationPose EstimationNeural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature
Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera and object undergo motion during capture…
Image-to-Image TranslationMotion EstimationLearning optical flow from still images
This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real videos. Specifically, we introduce a fr…
Depth EstimationMonocular Depth EstimationOptical Flow Estimation