We present a multi-camera system based on Bayesian modality fusion to track multiple people in an indoor environment. Bayesian networks are used to combine multiple modalities for matching subjects between consecutive image frames and between multiple camera views. Unlike other occlusion reasoning methods, we use multiple cameras in order to obtain continuous visual information of people in either or both cameras so that they can be tracked through interactions. Results demonstrate that the system can maintain people's identities by using multiple cameras cooperatively.
Tracking multiple people with a multi-camera system
2001-01-01
950356 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Tracking Multiple People with a Multi-Camera System
British Library Conference Proceedings | 2001
|Multiple camera fusion for multi-object tracking
IEEE | 2001
|Multiple Camera Fusion for Multi-Object Tracking
British Library Conference Proceedings | 2001
|OpenPTrack: Open Source Multi-Camera Calibration and People Tracking for RGB-D Camera Networks
BASE | 2016
|Multi-modal tracking of people using laser scanners and video camera
British Library Online Contents | 2008
|