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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 layout. Through a modulation of the sensor's...
This paper develops a compressed infrared motion sensing system for mobile robots to detect and localize a moving human target in the vicinity. The proposed sensing system consists of three compressed infrared bearing sensor arrays for generating the bearing measurements of a human target from three perspectives. Then, human location is inferred by fusing these bearing measurements with least square...
This paper studies the optimal design of an infrared motion sensing system for human motion localization in the context of human-following robots. Specifically, we aim to find the optimal number and placement of bearing-sensitive pyroelectric infrared (PIR) sensor arrays for improving localization performance. This optimal design leads to a multiobjective, mixed-integer-discrete-continuous and variable-dimensional...
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