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Distributed binary pyroelectric sensor network (PSN) is a low-cost alternative to video systems for human monitoring applications. This paper presents a PSN-based mobile target recognition system, which aims to achieve multitarget, complex scenario recognition. In this system, a novel pseudorandom visibility mode is designed for the sensor arrays to help capture statistical information of scenarios,...
We have established a multi-walker recognition/tracking testbed based on low-cost pyroelectrc sensor network (PSN). In order to identify a region of interest (Rol) in the monitoring area for the detection of any interesting mobile targets, we propose to use Bayesian machine learning and binary signal projection to extract the statistical contextual features from real-time, high-dimensional PSN data...
We have built a distributed binary pyroelectric sensor network (PSN) for the purpose of multi-walker recognition and tracking. It is important to identify a region of interest (RoI) in the monitoring area in order to find any interesting targets (i.e., walkers). The prerequisite of RoI identification is to accurately extract context features (such as the target IDs and positions) from a hybrid, binary,...
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