CVPR 2011 Workshops 2011
DOI: 10.1109/cvprw.2011.5981728
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A reasoning engine for intruders' localization in wide open areas using a network of cameras and RFIDs

Abstract: Wide open areas represent challenging scenarios for surveillance systems, since sensory data can be affected by noise, uncertainty, and distractors. Therefore, the tasks of localizing and identifying targets (e.g., people) in such environments suggest to go beyond the use of camera-only deployments. In this paper, we propose an innovative system relying on the joint use of cameras and RFIDs, allowing us to "map" RFID tags to people detected by cameras and, thus, highlighting potential intruders. To this end, s… Show more

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Cited by 5 publications
(1 citation statement)
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“…Anne et al [35] propose indoor person localization by combining computer vision, WiFi signal strength and RFID, where RFID tags are installed in the floor and RFID reader is placed in individual's shoes. Cucchiara et al [36] propose a combination of a network of video cameras and an active RFID system for detection of intruders in wide open areas. However, contrary to the system we propose, their system is not concerned with localization of individuals; their division of area of interest in relatively crude locations is done solely to allow association of detections from video cameras with those from the RFID system.…”
Section: Related Workmentioning
confidence: 99%
“…Anne et al [35] propose indoor person localization by combining computer vision, WiFi signal strength and RFID, where RFID tags are installed in the floor and RFID reader is placed in individual's shoes. Cucchiara et al [36] propose a combination of a network of video cameras and an active RFID system for detection of intruders in wide open areas. However, contrary to the system we propose, their system is not concerned with localization of individuals; their division of area of interest in relatively crude locations is done solely to allow association of detections from video cameras with those from the RFID system.…”
Section: Related Workmentioning
confidence: 99%