Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Visibility-Based Observation Model for 3D Tracking with Non-parametric 3D Particle Filters
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Particle filtering with multiple and heterogeneous cameras
Pattern Recognition
M2SIR: a multi modal sequential importance resampling algorithm for particle filters
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
| Hi-index | 0.00 |
Occlusion is a difficult problem for visual tracking and we use multiple wide baseline cameras to deal with occlusion. We propose a data fusion approach for visual tracking using multiple cameras with overlapping fields of view. First, we present a spatial and temporal recursive Bayesian filter to fuse information from multiple cameras. An adaptive particle filter is formulated to realize the spatial and temporal recursive Bayesian filter. Our algorithm is able to recover the target's position even under complete occlusion in a camera.