contours and optical flow: cues for capturing human motion in videos thomas brox computer vision and...
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Contours and Optical Flow: Cues for Capturing Human Motion in Videos
Thomas Brox
Computer Vision and Pattern Recognition GroupUniversity of Bonn
Research partially funded by the German Research Foundation (DFG)
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Thomas Brox
University of Bonn
Human pose tracking from video
1. Tracking of markers attached to the body+ Designed to be easy to track
Reliable and fast tracking
– Accuracy limited by number of markers
– People may feel uncomfortable
2. Tracking features that naturally appear in the images• Patches (e.g. KLT, SIFT, etc.)• Contour/Silhouette• Optic flow
How to extract these features reliably from the images
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Contour and optic flow based human tracking
Joint work with Bodo Rosenhahn
• Introduction
• Segmentation
• Optic Flow
• Summary
Part I
Object Contour Extraction
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Thomas Brox
University of Bonn
Object contour extraction
• Find two regions: object & background
• Often: Static background background subtraction
• Optimality criteria here:– Strong similarity within regions– Small boundary
• Bayesian approach:
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Level set representation of contours(Dervieux-Thomasset 1979, Osher-Sethian 1988)
• Introduce embedding function • Contour C represented as zero-level line of
Courtesy of Daniel Cremers
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Region-based active contours (Chan-Vese 2001, Paragios-Deriche 2002)
• Minimize negative logarithm:
• Gradient descent:
plus update of p1 and p2
H(x)
H’(x)
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Region statistics
• 7 channels:– 3 color channels (CIELAB)– 4 texture channels
• Channels assumed to be independent
• Probability densities pij approximated by Gaussians
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Texture
• Usually modeled by Gabor filters(Gabor 1946)
• Includes1. Magnitude2. Orientation3. Scale
• High redundancy
• Sparse alternative representation feasible• Nonlinear structure tensor
(Brox et al. 2006)
• Region based local scale measure(Brox-Weickert 2004)
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Sparse texture features
Gabor filter bank Sparse representation
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Examples for contour extraction
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Local region statistics
• Object and background usuallynot homogeneous
• Idea: assume them to be locally homogeneous
• Probability densities estimated by local Gaussians
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Introducing a shape prior
• Idea: object model can serve as 3-D shape prior Constrains the segmentation, unwanted solutions
discarded
• Bayesian formula:
• Pose parameters of model unknown Two variables: contour and pose
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
• Simultaneously optimize contour and pose:
• Iterative alternating scheme:– Update contour– Update pose parameters
• Related works: 2-D shape priors (Leventon et al. 2000, Cremers et al. 2002, Rousson-Paragios 2002)
Joint optimization
shape+pose constraint
conventional segmentation part
• Introduction
• Segmentation
• Optic Flow
• Summary
Part II
Optic Flow
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Thomas Brox
University of Bonn
Optic flow based tracking
Image 1 and 2, estimate flow in between Given pose at Image 1
Estimated pose at Image 2Pose change due to optic flow
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Tracking example
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
How to compute the optic flow?
• Given: two images I(x,y,t) and I(x,y,t+1) in a sequence
• Goal: displacement vector field (u,v) between these images
• Variational approach: (Horn-Schunck 1981)
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Enhanced model(Brox et al. 2004, Papenberg et al. 2006)
Robust smoothness term (Cohen 1993, Schnörr 1994)
Robust data term (Black-Anandan 1996, Mémin-Pérez 1996)
Spatiotemporal smoothness (Nagel 1990)
Gradient constancy (Brox et al. 2004)
Original Horn-Schunck:
Final optic flow model:
Non-linearized constancy (Nagel-Enkelmann 1986, Alvarez et al. 2000)• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Horn-SchunckRobust smoothness
Impact of each improvement
Correct result Robust data termGradient constancyNonlinear constancySpatiotemporal smoothness
7.1
7
6.3
6
5.9
7
3.5
2.4
4
1.7
8
0
2
4
6
8 Horn-Schunck
Robust smoothness
Robust data term
Gradient constancy
Nonlinear constancy
Spatio-temporal smoothness
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Accurate and robust optic flow computation
Technique AAE
Nagel 10.22°
Uras et al. 8.94°
Alvarez et al. 5.53°
Mémin-Pérez 4.69°
Brox et al. (Noisy) 4.49°
Bruhn et al. 4.17°
Brox et al. 1.78°
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Contour and optic flow based human tracking
Joint work with Bodo Rosenhahn
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Summary
• Contours and optic flow can be reliable features for pose tracking
• Texture, local statistics, and a shape prior are important for general contour based human motion tracking
• High-end optic flow helps in case of fast motion
What’s next?
• Real-time performance• Automatic pose initialization• Prior knowledge about joint angle configurations
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Outlook
Joint work with Bodo Rosenhahn
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Backup: nonlinear structure tensor
• Texture orientation can be measured with the structure tensor (second moment matrix)(Förstner-Gülch 1987, Rao-Schunck 1991, Bigün et al. 1991)
• Gaussian smoothing nonlinear diffusion
Input image Linear structure tensor
Nonlinear structure tensor
• Introduction
• Segmentation
• Optic Flow
• Summary
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Thomas Brox
University of Bonn
Backup: region based local scale measure
• Estimate regions, measuretheir size
• Nonlinear diffusion: TV flow(Andreu et al. 2001)
• Tends to yield piecewise constant images regions
• Local evolution speed inverselyproportional to size of region(Steidl et al. 2004)
local scale measure
• Introduction
• Segmentation
• Optic Flow
• Summary
Input image
Local scale