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 s from the moment of entry, as well as a performance of 22.1 minimum average precision (mAp) in human detection, 95.12% accuracy in face recognition, 93.3% Median Distance-Precision (MDP) in human tracking, and 10.80 minimum k average displacement error (MinADE₂₀), 18.55 minimum k final displacement error (MinFDE₂₀), 15.8 MinADE₅ and 30.50 MinFDE₅ in trajectory prediction.

近年来，家庭各个部分的智能化已成为任何现代家庭的基本特征之一。其中之一是智能照明系统，可以为每个人提供个性化的灯光。本文提出了一种基于机器学习的智能系统，可通过轨迹预测推断出已识别用户的即时未来位置的个性化照明。我们提出的系统由以下模块组成：(I) 人体检测，用于检测和定位每个给定视频帧中的人，(II) 人脸识别，用于识别检测到的人，(III) 人体跟踪，用于按序列跟踪人视频帧和 (IV) 轨迹预测，使用逆强化学习来预测用户在环境中的未来位置。所提出的方法为每个人提供了独特的配置文件，包括规格、面部图像和自定义照明设置。该配置文件用于照明调整过程。与考虑为每个人提供恒定照明的其他方法不同，我们的系统可以在颜色和光强度方面应用每个人所需的照明，而无需用户直接干预。因此，调光速度更快、效率更高。此外，预测的轨迹路径使建议的系统应用所需的照明，为家庭居民创造更加愉快和舒适的条件。实验结果显示，系统从进入开始的平均时间为1.4秒，即可应用所需的照明，人体检测的最低平均精度（mAp）为22.1，人脸识别准确率为95.12%，人脸识别准确率为93.3%人体跟踪中的中值距离精度 (MDP)，以及轨迹预测中的 10.80 最小 k 平均位移误差 (MinADE ₂₀ )、18.55 最小 k 最终位移误差 (MinFDE ₂₀ )、15.8 MinADE ₅和 30.50 MinFDE ₅。