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Parallelizing the Condensation Algorithm for Visual Tracking

Parallelizing the Condensation Algorithm for Visual Tracking. Amay Champaneria MIT 6.338 - Parallel Scientific Computing Monday, May 13, 2002 http://money.lcs.mit.edu/~amayc/partracker/. The Problem: Pen-Tracking. Track the tip of a pen

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Parallelizing the Condensation Algorithm for Visual Tracking

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  1. Parallelizing the Condensation Algorithm for Visual Tracking Amay Champaneria MIT 6.338 - Parallel Scientific Computing Monday, May 13, 2002 http://money.lcs.mit.edu/~amayc/partracker/

  2. The Problem: Pen-Tracking • Track the tip of a pen • Given a model of the pen tip, search the image for the best match • Where to search? • Previous approaches: • Maximum correlation • Kalman filter

  3. The Condensation Algorithm • Developed by Isard and Blake (IJCV 1998) • Condensation = Conditional Density Propogation • Estimate the future probability density by sampling from the current density and weighting the samples by some measure of their likelihood • Visualization…

  4. Visualization of Condensation

  5. Why Parallelize? • Condensation involves taking independent samples (a Monte-Carlo method) • More samples -> higher accuracy, more robust tracking • Allows us to track against cluttered backgrounds

  6. SerialCondens() { obtain_observations() predict_new_bases() calculate_weights() update() } ParallelCondens() { obtain_observations() Bcast(observations) predict_and_observe() Gather(results) calculate_weights() update() Bcast(updated_results) } Serial vs. Parallel

  7. And higher accuracy than Kalman Results

  8. Tracking Demonstration

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