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In recent years, the intelligence of various parts of the home has become oneof the essential features of any modern home. One of these parts is theintelligence lighting system that personalizes the light for each person. Thispaper proposes an intelligent system based on machine learning thatpersonalizes lighting in the instant future location of a recognized user,inferred by trajectory prediction. Our proposed system consists of thefollowing modules: (I) human detection to detect and localize the person ineach given video frame, (II) face recognition to identify the detected person,(III) human tracking to track the person in the sequence of video frames and(IV) trajectory prediction to forecast the future location of the user in theenvironment using Inverse Reinforcement Learning. The proposed method providesa unique profile for each person, including specifications, face images, andcustom lighting settings. This profile is used in the lighting adjustmentprocess. Unlike other methods that consider constant lighting for every person,our system can apply each 'person's desired lighting in terms of color andlight intensity without direct user intervention. Therefore, the lighting isadjusted with higher speed and better efficiency. In addition, the predictedtrajectory path makes the proposed system apply the desired lighting, creatingmore pleasant and comfortable conditions for the home residents. In theexperimental results, the system applied the desired lighting in an averagetime of 1.4 seconds from the moment of entry, as well as a performance of22.1mAp in human detection, 95.12% accuracy in face recognition, 93.3% MDP inhuman tracking, and 10.80 MinADE20, 18.55 MinFDE20, 15.8 MinADE5 and 30.50MinFDE5 in trajectory prediction.

DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home