Calibrating pan-tilt cameras in wide-area surveillance networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A master-slave system to acquire biometric imagery of humans at distance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
A virtual potential field based coverage algorithm for directional networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Re-identification with RGB-D sensors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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We propose a distributed, multi-camera video analysis paradigm for aiport security surveillance. We propose to use a new class of biometry signatures, which are called soft biometry including a person's height, built, skin tone, color of shirts and trousers, motion pattern, trajectory history, etc., to ID and track errant passengers and suspicious events without having to shut down a whole terminal building and cancel multiple flights. The proposed research is to enable the reliable acquisition, maintenance, and correspondence of soft biometry signatures in a coordinated manner from a large number of video streams for security surveillance. The intellectual merit of the proposed research is to address three important video analysis problems in a distributed, multi-camera surveillance network: sensor network calibration, peer-to-peer sensor data fusion, and stationary-dynamic cooperative camera sensing.