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Activity Recognition for Smart-Lighting Automation at Home

There is an undeniable movement to blur the line between everyday objects, infrastructure, and technology. We expect our daily interaction to be grounded in an intelligent system that adapts to its environment. Our homes are now becoming smarter through smart devices, such as televisions, speakers, light bulbs, and doors, connected through the Internet of Things, enabling increased home automation. In this paper, we describe a prototype system that analyzes an image of a living space to determine the activities of the occupants to control the lighting accordingly. Most available home-automation techniques require either explicit human control or rely on simple “if this then that” routines, based on basic environmental conditions, such as temperature or time of day. Other home-automation systems use ambient sensors, placed throughout the home to recognize what the user is doing. In this paper, we present a system that takes advantage of recent advancements in vision-based activity recognition to remove the explicit human control or multitude of sensors required by other systems. Knowing full well that no system will be perfect for every use, our system provides users the ability to change the lighting through explicit interaction; users' explicit lighting adjustments are recorded, to enable future system performance adjustments.