Multiple cameras are needed to cover large environments for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial relationships between the camera fields of view and use this information to correspond between different perspective views of the same person. We employ the novel approach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this information, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be visible. Moreover, we apply the FOV constraint to disambiguate between possible candidates of correspondence. We present results on sequences of up to three cameras with multiple people. The proposed approach is very fast compared to camera calibration based approaches.
Human tracking in multiple cameras
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 1 ; 331-336 vol.1
2001-01-01
1013401 byte
Conference paper
Electronic Resource
English
Human Tracking in Multiple Cameras
British Library Conference Proceedings | 2001
|Tracking across multiple cameras with disjoint views
IEEE | 2003
|Tracking across Multiple Cameras with Disjoint Views
British Library Conference Proceedings | 2003
|Tracking the soccer ball using multiple fixed cameras
British Library Online Contents | 2009
|Continuously Tracking Objects Across Multiple Widely Separated Cameras
Springer Verlag | 2007
|