Object tracking is an active and expanding research area in the computer vision community. It plays a critical role in applications such as monitoring and surveillance, human-computer interfaces, biomedical image analysis, mobile robots, etc. The probabilistic approach to tracking is attracting increasing research due to both its theoretical appeal and its practical success. The objective of this special session is to bring together researchers working on all aspects of object tracking, with a particular focus on probabilistic approaches.

Topics of interest include but are not limited to:

* Non-linear and non-Gaussian estimation using particle filters
* Kalman filtering and extensions for object tracking
* New probabilistic models for object dynamics or for the observation process
* New probabilistic interpretations of tracking algorithms
* Novel object tracking algorithms and technologies
* Applications of object tracking to real-world problems