CNN-Based Indoor Occupant Localization via Active Scene Illumination

We propose and study a data-driven approach to indoor occupant localization using a network of single-pixel light sensors and modulated LED light sources. Locations are estimated by processing sensor data using a simple convolutional neural network (CNN). Unlike previous model-based methods, the proposed approach does not require knowledge of room dimensions, locations of LEDs and sensors, and assumptions about material properties and object heights. We quantitatively validate the performance of our approach in simulated and real-world environments in private and public scenarios. In Unity3D simulations, compared to the best-performing benchmark method, our approach reduces the average localization error by 47.69% in private scenarios and by 46.99% in public scenarios. Similarly, in a real testbed the error is reduced by 36.54% and 11.46% in private and public scenarios respectively.