Optimal Scalar Quantization for Motion Tracking via Boolean Compressive Infrared Sampling
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.
Motion tracking Reference structure tomography Non-uniform quantization Optimization
LIU Jun LIU Min GUO Xuemei WANG Guoli
Laboratory for Information Processing & Human-Robot Systems,School of Information and Science Techno Laboratory for Information Processing & Human-Robot Systems,School of Information and Science Techno
国际会议
大连
英文
713-720
2011-10-19（万方平台首次上网日期，不代表论文的发表时间）