Optimal Scalar Quantization for Motion Tracking via Boolean Compressive Infrared Sampling

Abstract:

The recently emerged compressive sensing framework aims to acqure signals at reduced sample rates compared to the classical Shannon-Nyquist rate. This paper is concerned with the optimization problem of motion tracking driven by a sort of passive infrared sampling paradigm which springs from compressive sensing framework. In particular, a structured implementation with binary passive infrared sensor node is proposed to acquire the sparse presence state of human motion with resolution of some small cells. To calibrate the cells, a functional scalar quantizer is employed. Its optimization is used to adjust the design of sampling model to reduce the uncertainty of measurement noise. Simulation results show that the proposed optimization method benefits tracking accuracy to a certain degree.

• Periodical: Applied Mechanics and Materials (Volumes 148-149)
• Pages: 713-720
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.148-149.713
• Online since: December 2011
• Authors: Jun Liu, Min Liu, Xue Mei Guo, Guo Li Wang
• Keywords: Motion Tracking, Non-Uniform Quantization, Optimization, Reference Structure Tomography

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