Abstract
Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter.
Original language | English (US) |
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Pages (from-to) | 838-849 |
Number of pages | 12 |
Journal | IEEE Transactions on Image Processing |
Volume | 16 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2007 |
Keywords
- Particle filter
- Rao-Blackwellization
- Video-based surveillance
- Visual tracking
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design