Application of A Priori Map in Dynamic Background Removal for Indoor Human Perception Using a Thermopile Array

Human perception is of great research and application value. Thermopile infrared array sensor (TPAS) is an available solution without privacy invasion. In specific indoor environment like apartment and office, there are inevitable interfering thermal sources except human. Extracting human from the dynamically changing background i.e. background removal is a critical process. Based on the hardware platform built by a thermopile infrared array sensor MLX90640 with an output of
${24}\times {32}$
pixels, this paper proposes an adaptive Gaussian background removal algorithm applying a priori map (AGBR-PM) for indoor single human perception. For each static thermal source, an a priori map is established containing its location and thermal radiation feature in the way of support degree matrix. We build a Gaussian background model consisting of mean and standard deviation matrix. Each frame of thermal image is segmented into foreground and background based on
$2\sigma $
rule. A state and cross detection algorithm is proposed to compensate the error caused by interfering static thermal sources, and an erosion-connected component analysis and foreground complement algorithm is adopted to reduce the random noise as well. A regional update strategy is designed to adapt to the emergence of random thermal sources in background. The experimental results have shown that AGBR-PM has an excellent adaptability to dynamically changing environment, and can remove the background correctly.