1 robust visual tracking a brief summary gagan mirchandani school of engineering, university of...
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ROBUST VISUAL TRACKING
A Brief Summary
Gagan MirchandaniSchool of Engineering, University of Vermont
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1 And Ben Schilling, Clark Vandam, Kevin Haupt
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[1] J.Wright, A.Y.Yang, A.Ganesh, S.S.Sastry and Y.Ma, "Robust Face Recognition via Sparse Representation" IEEE Trans. PAMI , Feb. 2009, Vol.31, Issue:2, pp.210-227.
[2] X.Mei and H.Ling, "Robust Visual Tracking and Vehicle Classication via Sparse Representation" IEEE Trans. PAMI , Nov. 2011, Vol.33, Isssue:11, pp.2259-2272.
[3] Ben Schilling, Clark Vandam, Kevin Haupt
Algorithms from [1],[2]. Examples from [2].Videos from [3].
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1. Introduction
• Background, Goals
• Tracking and Recognition - important topics in Computer Vision
• Studied for decades
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2. Problem Areas
•• Tracking, recognition and counting objects (pedestrians, vehicles, bicyclists, etc. etc.)
• Needed for Policy determination, optimal traffic management, reduction of fuel, CO2 emission, etc.
• Needed for Surveillance
• Needed for Robotics
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3. Challenges
• Occlusion, noise, cluttered real-world environment
• Illumination change, many people, changing pose
• Changing background, real-time online implementation
• Computational complexity grows exponentially
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4. Theory
Basic problem: Given measurements y - Find x
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Bayesian State Estimation
If f and h linear (and noise Gaussian) then we get the Kalman filter
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Target candidate represented as sum of 10 templates (from previous frames) and trivial templates
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Estimation Method•Particle filters numerically generate
the particles
• according to the pdf
•This is tracking. The particle filter propagates sample pdfs over time
•Computational effort often a bottleneck
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5. Examples & Videos
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Target candidate represented as sum of 10 templates (from previous frames) and trivial templates
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Person walking; passing pole, high grass, body movement, occlusion.
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Fast moving car with significant scale changes
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Video taken from car in the back. Doll has pose & scale change and occlusion
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L1, MS, CV, AAPF & ES Trackers
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Drastic illumination change
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Partial occlusion, background clutter
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Severe occlusion
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Face rotates 180 . Car moves out of frame.o
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QuickTime™ and a decompressor
are needed to see this picture.
QuickTime™ and a decompressor
are needed to see this picture.
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QuickTime™ and a decompressor
are needed to see this picture.
QuickTime™ and a decompressor
are needed to see this picture.
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Questions?