CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Fast Lighting Independent Background Subtraction
International Journal of Computer Vision - Special issue on a special section on visual surveillance
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Exemplar-Based Face Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Identification of humans using gait
IEEE Transactions on Image Processing
Using hidden Markov models for recognizing action primitives in complex actions
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Hi-index | 0.00 |
In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data during tracking. We eploy a particle filter for density estimation. We have extensively tested our approach on suitable outdoor video data.