Towards Robust Indoor and Outdoor Smart Environments

This thesis presents robust machine learning and deep learning methods for the analysis of various indoor and outdoor activities to establish a smart environment. A smart environment includes different kinds of sensors, devices, appliances, and embedded systems to perceive its surroundings and make decisions. To further improve the robustness of smart environments, various deep learning architectures are proposed with specific requirements of either accuracy, computational complexity, or memory limitations.