  In recent years, the intelligence of various parts of the home has become one
of the essential features of any modern home. One of these parts is the
intelligence lighting system that personalizes the light for each person. This
paper proposes an intelligent system based on machine learning that
personalizes lighting in the instant future location of a recognized user,
inferred by trajectory prediction. Our proposed system consists of the
following modules: (I) human detection to detect and localize the person in
each 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 the
environment using Inverse Reinforcement Learning. The proposed method provides
a unique profile for each person, including specifications, face images, and
custom lighting settings. This profile is used in the lighting adjustment
process. Unlike other methods that consider constant lighting for every person,
our system can apply each 'person's desired lighting in terms of color and
light intensity without direct user intervention. Therefore, the lighting is
adjusted with higher speed and better efficiency. In addition, the predicted
trajectory path makes the proposed system apply the desired lighting, creating
more pleasant and comfortable conditions for the home residents. In the
experimental results, the system applied the desired lighting in an average
time of 1.4 seconds from the moment of entry, as well as a performance of
22.1mAp in human detection, 95.12% accuracy in face recognition, 93.3% MDP in
human tracking, and 10.80 MinADE20, 18.55 MinFDE20, 15.8 MinADE5 and 30.50
MinFDE5 in trajectory prediction.
