Mobile Target Scenario Recognition Via Low-Cost Pyroelectric Sensing System: Toward a Context-Enhanced Accurate Identification

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, and a sensor array fusion scheme is adopted to facilitate discriminative feature extraction. Moreover, we propose a statistical subspace representation model called probabilistic nonnegative matrix factorization (PNMF) to seek the scenario patterns rather than the object characteristics. We also further prove that our PNMF model is a generic model for NMF based algorithms. Original NMF, sparse NMF, and smooth NMF are special cases of the PNMF model. The simulation and experimental results demonstrate the advantages of our proposed method. Our system can be further developed to function as an independent facility for intelligent monitoring applications, especially under poor illumination circumstances.