Mutual calibration of camera motes and RFIDs for people localization and identification

Abstract

Achieving both localization and identification of people in a wide open area using only cameras can be a challenging task, which requires cross-cutting requirements : high resolution for identification, whereas low resolution for having a wide coverage of the localization. Consequently, this paper proposes the joint use of cameras (only devoted to localization) and RFID sensors (devoted to identification) with the final objective of detecting and localizing intruders. To ground the observations on a common coordinate system, a calibration procedure is defined. This procedure only demands a training phase with a single person moving in the scene holding a RFID tag. Although preliminary, the results demonstrate that this calibration is sufficiently accurate to be applied whenever different scenarios, where area of overlap between the field of view (FoV) of a camera and the "field of sense" (FoS) of a (blind) sensor must be efficiently determined.

Video Summarization for Object Tracking in the Internet of Things

“…Even if RFID tags are missing in the new video, the learned models can be used for fuzzy visual pattern recognition of activities and objects. The work in [12] applies multiple cameras for tracking people. RFID antennas are deployed to identify authorized people who carry active RFID tags.…”

A System for Multimodal Context-Awareness

“…RFID systems and WSNs can be combined together not only for identifying and localizing objects, but also for real-time monitoring [24]. To identify and localize in open areas, researchers of [2] derived a calibration method for a joint RFID-camera system based on the area of overlap between the field of view (FoV) of a camera and the field of sense (FoS) of RFID sensors.…”

“…The proliferation of ambient intelligent environments has triggered research related to applications, such as monitoring Assistive Daily Living (ADL), fall detection, risk prevention and surveillance [1,2]. For achieving these goals, activity recognition performed in a natural and unintrusive way is of utmost importance.…”

A reasoning engine for intruders' localization in wide open areas using a network of cameras and RFIDs

“…The latter requirement could be easily satisfied by a preliminary offline analysis of the paths typically covered by people in the area, for instance by reviewing previously captured videos. As an alternative, an automatic method to determine the locations which maximize their identification by RFIDs can be employed (see for instance the work in [6]). …”