Abstract
The human centric lighting (HCL) control is a major focus point of the smart lighting system design to provide energy efficient and people mood rhythmic motivation lighting in smart buildings. This paper proposes the HCL control using indoor surveillance camera to improve the human motivation and well-beings in the indoor environments like residential and industrial buildings. In this proposed approach, the indoor surveillance camera video streams are used to predict the day lights and occupancy, occupancy specific emotional features predictions using the advanced computer vision techniques, and this human centric features are transmitted to the smart building light management system. The smart building light management system connected with internet of things (IoT) featured lighting devices and controls the light illumination of the objective human specific lighting devices. The proposed concept experimental model implemented using RGB LED lighting devices connected with IoT features open-source controller in the network along with networked video surveillance solution. The experiment results are verified with custom made automatic lighting control demon application integrated with OpenCV framework based computer vision methods to predict the human centric features and based on the estimated features the lighting illumination level and colors are controlled automatically. The experiment results received from the demon system are analyzed and used for the real-time development of a lighting system control strategy.