基于视觉跟踪的实时视频人脸识别

目前基于深度学习的人脸识别方法准确率高,但是模型复杂,识别速度慢.为了实现监控视频中人脸的实时识别,提出了一种基于视觉跟踪的实时视频人脸识别(RFRV-VT)方法.首先将监控视频的帧序列分组,每一组中分为人脸识别帧和人脸跟踪帧;然后在人脸识别帧中使用基于深度学习的人脸检测和人脸特征提取方法,在人脸跟踪帧中使用基于核相关滤波(KCF)的视觉跟踪方法以加快识别速度.将该方法应用于数据集YouTube Faces(YTF)上进行测试,实验结果显示该算法在监控视频中具有实时性和较高的识别准确性(99.60%).福建省自然科学基金(2015J01288

Prediction of Energy Resolution in the JUNO Experiment

International audienceThis paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors