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This paper presents a novel human tracking scheme by using pyroelectric infrared sensors. The scheme includes the visibility modulation of each sensor detector, the layout of the system, the localization and tracking algorithms. The results from the 3D simulations using Webots and Matlab together validate our scheme, and comparisons with other related(More)
— Device-free motion tracking with radio tomographic networks using received signal strength (RSS) measurements has attracted considerable research efforts. Since the motion scene to be reconstructed can often be assumed sparse, i.e., it consists only of several targets, the Compressed Sensing (CS) framework can be applied. We cast the motion tracking as a(More)
Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted(More)
This paper presents a human indoor localization system using ceiling mounted pyroelectric infrared (PIR) sensors. The field of views (FOVs) of the PIR sensors is modulated by two degrees of freedom (DOF) of spatial segmentation. The localization algorithm is proposed to fuse the data stream generated from different sensor nodes within the wireless network.(More)
This paper presents a compressive classification approach for human motion by using pyroelectric infrared (PIR) sensors. We represent a human motion as a spatio-temporal energy sequence (STES) which is extracted from an infrared radiation domain. The proposed approach consists of two major parts: a compressive sensing unit and an orthogonal-view sensing(More)
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