Synchronization and rolling shutter compensation for consumer video camera arrays

Bradley, Derek; Atcheson, Bradley; Ihrke, Ivo; Heidrich, Wolfgang

doi:10.1109/cvpr.2009.5204340

Cited by 23 publications

(25 citation statements)

References 15 publications

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“…Specifically, as illustrated in Fig. 2, although each scanline is exposed for the same duration t e , the exposure window is offset by t r from scanline to scanline [4]. We thus rewrite Eq.…”

Section: Motion Blur In Rolling Shutter Camerasmentioning

confidence: 99%

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Rolling shutter motion deblurring

Heidrich

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Although motion blur and rolling shutter deformations are closely coupled artifacts in images taken with CMOS image sensors, the two phenomena have so far mostly been treated separately, with deblurring algorithms being unable to handle rolling shutter wobble, and rolling shutter algorithms being incapable of dealing with motion blur.We propose an approach that delivers sharp and undistorted output given a single rolling shutter motion blurred image. The key to achieving this is a global modeling of the camera motion trajectory, which enables each scanline of the image to be deblurred with the corresponding motion segment. We show the results of the proposed framework through experiments on synthetic and real data.

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Section: Motion Blur In Rolling Shutter Camerasmentioning

confidence: 99%

“…We chose a DSLR over a cell-phone because it gives us access to specific values for t e and t r , as needed by our method. We adopted the method in [4] to obtain the value of t r , which is determined by the frame rate and the total number of scanlines per frame.…”

Section: Real Imagesmentioning

confidence: 99%

Rolling shutter motion deblurring

Heidrich

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Here, Bradley et al [3] use stroboscope lighting and subframe warping to synchronize multiple rolling shutter cameras and to compensate the sequential exposure effects. Baker et al [2] pose the rectification as a superresolution problem that can be solved using optical flow.…”

Section: Previous Workmentioning

confidence: 99%

Rolling Shutter Stereo

Saurer

Köser

Bouguet

et al. 2013

2013 IEEE International Conference on Computer Vision

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A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing streetlevel capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.

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“…Excessive computational load due to the smoothing operation to compensate for the inaccuracy of motion vector is not acceptable in mobile devices" [6]. [5] proposed 2 methods for removing the spatio-temporal distortions in video sequences caused by rolling shutter cameras. The first method involved the use of active illumination which limits the applicability of the approach.…”

Section: A Rolling Shutter Camera Models and Distortion Compensationmentioning

confidence: 99%

Correcting rolling-shutter distortion of CMOS sensors using facial feature detection

Heflin

Scheirer

Boult

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Abstract-This paper proposes a fully automated post image processing scheme based on facial feature detection to correct the horizontal temporal shear or rolling shutter distortion. This distortion occurs when obtaining images or video sequences from a CMOS camera with a rolling shutter whenever there is relative horizontal movement between the sensor and the object being imaged during the integration time of the image frame. Unlike CCD sensors, such as the interline CCD, which provides an electronic shutter mechanism called a global shutter in which the light collection starts and ends at exactly the same time for all pixels, CMOS sensors can not hold and store all the pixels at the same time. Each scanline is exposed, sampled, and stored in sequence, resulting in the rolling shutter effect or temporal distortion of the image that will cause inaccurate facial recognition results.Facial feature detection is performed using correlation based methods with low computational complexity. The location of key facial feature points is then used to calculate the temporal horizontal shear or the distortion of the image. This information can then be used to remove the temporal horizontal shear distortion from the detected face or the entire image. We present experimental results on controlled data sets and real scenes to show that the proposed method yields excellent results in reversing the temporal horizontal shear caused by the CMOS rolling shutter sensor and significantly improves the accuracy of our facial recognition algorithm.

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Synchronization and rolling shutter compensation for consumer video camera arrays

Cited by 23 publications

References 15 publications

Rolling shutter motion deblurring

Rolling shutter motion deblurring

Rolling Shutter Stereo

Correcting rolling-shutter distortion of CMOS sensors using facial feature detection

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