1. Parallelizing the Condensation Algorithm for Visual Tracking
Amay Champaneria
MIT Parallel Scientific Computing
Monday, May 13, 2002

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

7. Results
And higher accuracy than Kalman

8. Tracking Demonstration