On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Towards Vision-Based 3-D People Tracking in a Smart Room
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Adaptive Particle Filter for Data Fusion of Multiple Cameras
Journal of VLSI Signal Processing Systems
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foreground regions extraction and characterization towards real-time object tracking
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
| Hi-index | 0.00 |
This paper presents a novel and powerful Bayesian framework for 3D tracking of multiple arbitrarily shaped objects, allowing the probabilistic combination of the cues captured from several calibrated cameras directly into the 3D world without assuming ground plane movement. This framework is based on a new interpretation of the Particle Filter, in which each particle represent the situation of a particular 3D position and thus particles aim to represent the volumetric occupancy pdf of an object of interest. The particularities of the proposed Particle Filter approach have also been addressed, resulting in the creation of a multi-camera observation model taking into account the visibility of the individual particles from each camera view, and a Bayesian classifier for improving the multi-hypothesis behavior of the proposed approach.