lecture23 - EECS 442 – Computer vision Optical flow and...

Info iconThis preview shows pages 1–18. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: EECS 442 – Computer vision Optical flow and tracking • Intro • Lucas-Kanade algorithm • Motion segmentation • Kalman filters Segments of this lectures are courtesy of Profs S. Lazebnik S. Seitz, R. Szeliski, M. Pollefeys, K. Hassan-Shafique. S. Thrun From images to videos • A video is a sequence of frames captured over time • Now our image data is a function of space (x, y) and time (t) Uses of motion • Estimating 3D structure • Tracking objects • Segmenting objects based on motion cues • Learning dynamical models • Recognizing events and activities • Improving video quality (motion stabilization) Estimating 3D structure Z.Yin and R.Collins, "On-the-fly Object Modeling while Tracking," IEEE Computer Vision and Pattern Recognition (CVPR '07), Minneapolis, MN, June 2007, 8 pages. Tracking objects Segmenting objects based on motion cues • Background subtraction – A static camera is observing a scene – Goal: separate the static background from the moving foreground • Motion segmentation – Segment the video into multiple coherently moving objects Segmenting objects based on motion cues S. J. Pundlik and S. T. Birchfield, Motion Segmentation at Any Speed, Proceedings of the British Machine Vision Conference 06 Motion and perceptual organization Learning dynamical models D. Ramanan, D. Forsyth, and A. Zisserman. Tracking People by Learning their Appearance . PAMI 2007. Tracker Recognizing events and activities Juan Carlos Niebles, Hongcheng Wang and Li Fei-Fei, Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words, ( BMVC ) , Edinburgh, 2006. Recognizing events and activities Motion estimation techniques • Optical flow – Recover image motion at each pixel from spatio- temporal image brightness variations (optical flow) • Feature-tracking – Extract visual features (corners, textured areas) and “track” them over multiple frames Picture courtesy of Selim Temizer- Learning and Intelligent Systems (LIS) Group, MIT Optical flow Vector field function of the spatio-temporal image brightness variations Feature-tracking Courtesy of Jean-Yves Bouguet – Vision Lab, California Institute of Technology Feature-tracking Courtesy of Jean-Yves Bouguet – Vision Lab, California Institute of Technology Optical flow Definition: optical flow is the apparent motion of brightness patterns in the image Note: apparent motion can be caused by lighting changes without any actual motion • Think of a uniform rotating sphere under fixed lighting vs. a stationary sphere under moving illumination Estimating optical flow iven two subsequent frames, estimate the apparent motion field u(x,y), v(x,y) between them Key assumptions • Brightness constancy: projection of the same point looks the same in every frame • Small motion: points do not move very far • Spatial coherence: points move like their neighbors I ( x , y , t –1) I ( x , y , t ) u(x,y) Brightness Constancy Equation:...
View Full Document

This note was uploaded on 10/26/2010 for the course EECS 442 taught by Professor Savarese during the Fall '09 term at University of Michigan.

Page1 / 62

lecture23 - EECS 442 – Computer vision Optical flow and...

This preview shows document pages 1 - 18. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online