DeePLT：智能家居中基于已识别居民轨迹预测的个性化照明促进
DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home
AI总结：
本文提出DeePLT系统，结合人体检测、人脸识别、跟踪和逆强化学习轨迹预测，在智能家居中为已识别居民个性化照明，无需干预，平均1.4秒内调整，提升舒适度和效率。
摘要：
近年来，家庭各部分的智能化已成为任何现代家庭的基本特征之一。其中一部分是智能照明系统，它能为每个人个性化灯光。本文提出了一种基于机器学习的智能系统，该系统通过轨迹预测推断已识别用户的即时未来位置，并在此位置个性化照明。我们提出的系统由以下模块组成：（I）人体检测，用于在每个给定视频帧中检测并定位人物；（II）人脸识别，用于识别检测到的人物；（III）人体跟踪，用于在视频帧序列中跟踪人物；（IV）轨迹预测，使用逆强化学习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一个独特的配置文件，包括规格、人脸图像和自定义照明设置。该配置文件用于照明调整过程。与其他认为每个人照明恒定不变的方法不同，我们的系统可以在无需用户直接干预的情况下，根据每个人的颜色和光强度应用其期望的照明。因此，照明调整具有更高的速度和更好的效率。此外，预测的轨迹路径使所提出的系统能够应用期望的照明，为家庭居民创造更愉快和舒适的条件。在实验结果中，系统从进入时刻起平均1.4秒内应用期望照明，人体检测性能为22.1mAp，人脸识别准确率为95.12%，人体跟踪MDP为93.3%，轨迹预测的MinADE20为10.80，MinFDE20为18.55，MinADE5为15.8，MinFDE5为30.50。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
