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ROBUST VISUAL TRACKING A Brief Summary

ROBUST VISUAL TRACKING A Brief Summary. 1. Gagan Mirchandani School of Engineering, University of Vermont. 1. And Ben Schilling, Clark Vandam, Kevin Haupt. Algorithms from [1],[2]. Examples from [2]. Videos from [3]. [1] J.Wright, A.Y.Yang, A.Ganesh, S.S.Sastry and Y.Ma,

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ROBUST VISUAL TRACKING A Brief Summary

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  1. ROBUST VISUAL TRACKINGA Brief Summary 1 Gagan Mirchandani School of Engineering, University of Vermont 1 And Ben Schilling, Clark Vandam, Kevin Haupt

  2. Algorithms from [1],[2]. Examples from [2]. Videos from [3]. [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

  3. 1. Introduction • Background, Goals • Tracking and Recognition - important topics in Computer Vision • Studied for decades • An accelerated algorithm for the simulation of the Ising Model is presented, based on a hierarchical decomposition of the model using the discrete wavelet transform, employing the Haar wavelet. An empirically derived relationship between the temperature in the original model, , and an optimal temperature in the wavelet decomposed model, , is discovered for various levels of wavelet decomposition. This temperature scaling relationship improves the accuracy of estimates of the thermodynamic quantities within the vicinity of the critical region as compared to previous methods.

  4. 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

  5. 3. Challenges • Occlusion, noise, cluttered real-world environment • Illumination change, many people, changing pose • Changing background, real-time online implementation • Computational complexity grows exponentially

  6. 4. Theory Basic problem: Given measurements y - Find x

  7. Bayesian State Estimation If f and h linear (and noise Gaussian) then we get the Kalman filter

  8. Target candidate represented as sum of 10 templates (from previous frames) and trivial templates

  9. 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

  10. 5. Examples & Videos

  11. Target candidate represented as sum of 10 templates (from previous frames) and trivial templates

  12. Person walking; passing pole, high grass, body movement, occlusion.

  13. Fast moving car with significant scale changes

  14. Video taken from car in the back. Doll has pose & scale change and occlusion

  15. L1, MS, CV, AAPF & ES Trackers

  16. Drastic illumination change

  17. Partial occlusion, background clutter

  18. Severe occlusion

  19. o Face rotates 180 . Car moves out of frame.

  20. Questions?

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