Neural Networks for Indoor Person Tracking With Infrared Sensors

Indoor localization has many pervasive applications, like energy management, health monitoring, and security. Tagless localization detects directly the human body, like passive infrared sensing, and is the most amenable to different users and use cases. We evaluate the localization and tracking performance, as well as resource and processing requirements of various neural network (NN) types using directly the data from a low resolution 16-pixel thermopile sensor array in a 3 m x 3 m room. Out of the multilayer perceptron, autoregressive, 1D-CNN, and LSTM NN architectures that we test, the latter require more resources but can accurately locate and capture best the person movement dynamics, while the 1D-CNN provides the best compromise between localization accuracy (9.6 cm RMSE) and movement tracking smoothness with the least resources, and seem more suited for embedded applications.