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James W. Pennebaker, Chair The University of Texas at Austin, SEA 4.212, Austin, TX 78712 • (512) 475-7596

Video Stabilization and Rectification for Handheld Cameras

Fri, April 4, 2014 • 1:00 PM - 3:00 PM • ENS 637

Chao Jia 

Department of Electrical & Computer Engineering

The University of Texas at Austin

 

4/4/2014

1:00 PM

ENS 637

 

Find information about current and upcoming talks at CPS on our website: 

 http://www.utexas.edu/cola/centers/cps/events/calendar.php

 

http://signal.ece.utexas.edu/~cjia/

 

Abstract:  Video data has increased dramatically in recent years due to the prevalence of handheld cameras. Such videos, however, are usually shakier compared to videos shot by tripod-mounted cameras or cameras with mechanical stabilizers. In addition, most handheld cameras use CMOS sensors. In a CMOS sensor camera, different rows in a frame are read/reset sequentially from top to bottom. When there is fast relative motion between the scene and the video camera, a frame can be distorted because each row was captured under a different 3D-to-2D projection. This kind of distortion is known as rolling shutter effect. Digital video stabilization and rolling shutter rectification seek to remove the unwanted frame-to-frame jitter and rolling shutter effect, in order to generate visually stable and pleasant videos. In general, we need to (1) estimate the camera motion, (2) regenerate camera motion, and (3) synthesize new frames. This dissertation aims at improving the first two steps of video stabilization and rolling shutter rectification.

 

It has been shown that the inertial sensors in handheld devices can provide more accurate and robust motion estimation compared to vision-based methods. This dissertation proposes an online camera-gyroscope calibration method for sensor fusion while a user is capturing video. The proposed method uses an implicit extended Kalman filter and is based on multiple-view geometry in a rolling shutter camera model. It is able to estimate the needed calibration parameters online with all kinds of camera motion.

 

Given the camera motion estimated from inertial sensors after the proposed calibration method, this dissertation first proposes an offline motion smoothing algorithm based on a 3D rotational camera motion model. The offline motion smoothing is formulated as a geodesic-convex regression problem on the manifold of rotation matrix sequences. The formulated problem is solved by an efficient two-metric projection algorithm on the manifold. The geodesic-distance-based smoothness metric better exploits the manifold structure of sequences of rotation matrices. Then this dissertation proposes two online motion smoothing algorithms that are also based on a 3D rotational camera motion model. The first algorithm extends IIR filtering from Euclidean space to the nonlinear manifold of 3D rotation matrices. The second algorithm uses unscented Kalman filtering on a constant angular velocity model. Both offline and online motion smoothing algorithms are constrained to guarantee that no black borders intrude into the stabilized frames.


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