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.

翻译：近年来，住宅各部分的智能化已成为现代家居的基本特征之一。其中，智能照明系统能够为每位住户提供个性化照明。本文提出一种基于机器学习的智能系统，通过轨迹预测推断已识别用户即将到达的位置，并在该位置实现个性化照明。系统包含以下模块：（I）人体检测——在每帧视频中检测并定位人员；（II）人脸识别——识别检测到的特定人员；（III）人体追踪——在连续视频帧中追踪该人员；（IV）基于逆向强化学习的轨迹预测——预测用户在环境中的未来位置。该方法为每位用户建立包含特征参数、人脸图像及自定义照明设置的独立配置文件，并应用于照明调节过程。不同于其他方法对所有人采用固定照明，本系统无需用户直接干预即可根据每位用户的偏好调节灯光颜色与亮度，从而实现更快速高效的照明调整。此外，轨迹预测路径使系统能够预先应用期望照明，为住户创造更宜人舒适的居住环境。实验结果显示：该系统从人员进入至应用期望照明的平均响应时间为1.4秒，人体检测性能达22.1mAp，人脸识别准确率95.12%，人体追踪MDP为93.3%，轨迹预测指标分别为MinADE20 10.80、MinFDE20 18.55、MinADE5 15.8及MinFDE5 30.50。