Abstract
Tracking involves estimating not only the global motion but also local perturbations or deformations corresponding to a specified object of interest. From this, motion can be decoupled into a finite dimensional state space (the global motion) and the more interesting infinite dimensional state space (deformations). Recently, the incorporation of the particle filter with geometric active contours which use first and second moments has shown robust tracking results. By generalizing the statistical inference to entire probability distributions, we introduce a new distribution metric for tracking that is naturally able to better model the target. Also, due to the multiple hypothesis nature of particle filtering, it can be readily seen that if the background resembles the foreground, then one might lose track. Even though this can be described as a finite dimensional problem where global motion can be modeled and learned online through a filtering process, we approach this task by incorporating a separate energy term in the deformable model that penalizes large centroid displacements. Robust results are obtained and demonstrated on several surveillance sequences.
Original language | English (US) |
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Title of host publication | Image Processing |
Subtitle of host publication | Machine Vision Applications |
Volume | 6813 |
DOIs | |
State | Published - Mar 31 2008 |
Event | Image Processing: Machine Vision Applications - San Jose, CA, United States Duration: Jan 29 2008 → Jan 31 2008 |
Other
Other | Image Processing: Machine Vision Applications |
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Country/Territory | United States |
City | San Jose, CA |
Period | 1/29/08 → 1/31/08 |
Keywords
- Distributions
- Geometric active contours
- Particle filtering
- Tracking, metrics