IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
Object Localization by Bayesian Correlation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-level Particle Filter Fusion of Features and Cues for Audio-Visual Person Tracking
Multimodal Technologies for Perception of Humans
Kernel Based Multi-object Tracking Using Gabor Functions Embedded in a Region Covariance Matrix
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple object tracking using HSV color space
Proceedings of the 2011 International Conference on Communication, Computing & Security
Tracking targets via particle based belief propagation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Sequential stratified sampling belief propagation for multiple targets tracking
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Hierarchical model for joint detection and tracking of multi-target
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Identification and tracking of robots in an intelligent space using static cameras and an XPFCP
Robotics and Autonomous Systems
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The recently proposed CONDENSATION algorithm and its variants enable the estimation of arbitrary multi-modal posterior distributions that potentially represent multiple tracked objects. However, the specific state representation adopted in the earlier work does not explicitly supports counting, addition, deletion and occlusion of objects. Furthermore, the representation may increasingly bias the posterior density estimates towards objects with dominant likelihood as the estimation progresses over many frames. In this paper, a novel formulation and an associated CONDENSATION-like sampling algorithm that explicitly support counting, addition and deletion of objects are proposed. We represent all objects in an image as an object configuration. The a posteriori distribution of all possible configurations are explored and maintained using sampling techniques. The dynamics of configurations allow addition and deletion of objects and handle occlusion. An efficient hierarchical algorithm is also proposed to approximate the sampling process in high dimensional space. Promising comparative results on both synthetic and real data are demonstrated.