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Elliptical Head Tracking Using Intensity Gradients and Color Histograms Stan Birchfield Stanford University Autodesk Advanced Products Group http://vision.stanford.edu/~birch
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PROBLEM TILT PAN ZOOM CHALLENGES: * rotation * multiple people * zoom APPLICATIONS: * video conferencing * distance learning
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PREVIOUS METHODS FLESH- COLORED OBJECTS MULTIPLE MOVING PEOPLE ARBITRARY CAMERA MOVEMENT OUT-OF-PLANE ROTATION 1. TEMPLATE [Hager & Belhumeur, 1996] YYYN 2. FLESH COLOR [Fieguth & Terzopoulos, 1997] NNYN 3. BACKGROUND DIFFERENCING [Graf et al., 1996] YNNY Method Criterion
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CUES: COLOR MOTION TEXTURE INTERIORBOUNDARY COMPLEMENTARY CRITERIA INTENSITY EDGES DEPTH & MOTION. DISCONTINUITIES
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APPLICATION: 1. Interesting, useful 2. Well-connected to other body parts WHY FOCUS ON THE HEAD? GEOMETRIC: 1. Nearly rigid 2. Nearly ellipsoid Easy to model
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HEAD MODEL (x,y) Ellipse: vertical aspect ratio = 1.2 state s = (x,y, ) SEARCH velocity prediction LOCAL HEAD SEARCH GRADIENTCOLOR SEARCH RANGE
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TWO CHOICES: 1. MAGNITUDE 2. DOT PRODUCT NORMALIZATION GRADIENT MODULE ellipse normalgradient
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COLOR MODULE COLOR SPACE HISTOGRAM INTERSECTION [Swain & Ballard 1991] NORMALIZATION B-G (8 bins)G-R (8 bins) B+G+R (4 bins) MODEL CURRENT INTERSECTION SKINHAIR
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SUMMARY OF ALGORITHM OFF-LINE: 1. Manually place head within ellipse 2. Store model histogram RUN TIME: 1. At each hypothesized location, compute - Sum of gradient around perimeter - Histogram intersection 2. Move ellipse to location that maximizes sum of two criteria
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COMPARISON OF MODULES Controls pan, tilt, zoom Handles textured backgrounds More robust Large basin of attraction Controls pan, tilt Keeps off neck Scale in front of flesh-colored object Scale when back turned COLORGRADIENT
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BASIN OF ATTRACTION Gradient confused, pulls to leftColor pulls to right
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COMPUTING TIME Real time (30 Hz) Computing time per frame (ms) Search range (on a 200 MHz Pentium Pro)
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CONCLUSION SUCCESSES: 1. Tracks head in real time on standard hardware 2. Insensitive to - full 360-degree out-of-plane rotation - arbitrary camera movement (including zoom) - multiple moving people - severe but brief occlusion - hair/skin color, hair length, facial hair, glasses FUTURE WORK: 1. Speed (computer speed and NTSC video standard) 2. Color adaptation, but imprecise localization 3. No explicit model of occlusion
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