监控视频中人体目标运动轨迹提取技术

提出一类基于监控视频的人体目标运动轨迹的自动提取技术:首先采用背景差分算法检测监控视频关键帧,并提取人体目标;然后对包含人体目标的监控视频关键帧进行数学形态学的开/闭运算后估计人体目标的质心;最后通过质心叠加实现了对监控视频中人体目标运动轨迹的自动提取

面向视频侦查应用的监控视频关键帧检测软件设计

为解决视频侦查工作中由于监控视频数据量大所导致的案件相关信息获取实时性差、误判与漏判等问题，利用数据流图深入分析了视频侦查工作对关键帧检测的需求，在关联数据流图、顸层数据流图与统计分析表的基础之上，提出了能够满足现阶段视频侦查应用的监控视频关键帧检测软件的总体设计和人机交互界面设计方案

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