Active scene illumination metods for privacy-preserving indoor occupant localization

Indoor occupant localization is a key component of location-based smart-space applications. Such applications are expected to save energy and provide productivity gains and health benefits. Many traditional camera-based indoor localization systems use visual information to detect and analyze the states of room occupants. These systems, however, may not be acceptable in privacy-sensitive scenarios since high-resolution images may reveal room and occupant details to eavesdroppers. To address visual privacy concerns, approaches have been developed using extremely-low-resolution light sensors, which provide limited visual information and preserve privacy even if hacked. These systems preserve visual privacy and are reasonably accurate, but they fail in the presence of noise and ambient light changes. This dissertation focuses on two-dimensional localization of an occupant on the floor plane, where three goals are considered in the development of an indoor localization system: accuracy, robustness and visual privacy preservation. Unlike techniques that preserve user privacy by degrading full-resolution data, this dissertation focuses on an array of single-pixel light sensors. Furthermore, to make the system robust to noise, ambient light changes and sensor failures, the scene is actively illuminated by modulating an array of LED light sources, which allows algorithms to use light transported from sources to sensors (described as light transport matrix) instead of raw sensor readings. Finally, to assure accurate localization, both principled model-based algorithms and learning-based approaches via active scene illumination are proposed. In the proposed model-based algorithm, the appearance of an object is modeled as a change in floor reflectivity in some area. A ridge regression algorithm is developed to estimate the change of floor reflectivity from change in the light transport matrix caused by appearance of the object. The region of largest reflectivity change identifies object location. Experimental validation demonstrates that the proposed algorithm can accurately localize both flat objects and human occupants, and is robust to noise, illumination changes and sensor failures. In addition, a sensor design using aperture grids is proposed which further improves localization accuracy. As for learning-based approaches, this dissertation proposes a convolutional neural network, which reshapes the input light transport matrix to take advantage of spatial correlations between sensors. As a result, the proposed network can accurately localize human occupants in both simulations and the real testbed with a small number of training samples. Moreover, unlike model-based approaches, the proposed network does not require modeling assumptions or knowledge of room, sources and sensors