Video Detection Algorithm Research and Practice of Traffic Incident

交通事件检测系统在智能交通系统中有着重要的意义。交通事件检测的方法有很多，其中视频检测是近几年发展最为迅速的技术之一。因为它具有安装方便、覆盖范围大、效果好等特点，所以已经成为智能交通系统领域的一个研究热点。 本文围绕对交通事件视频检测系统的实现，研究了一些车辆检测和跟踪的方法，提出了一些新的算法。主要的工作和贡献有： [1]运动车辆的检测。针对不同的情况，本文运用了两种不同的背景初始化方法，然后采用一种动态更新背景的方法对背景进行实时的更新。运用背景差分、膨胀腐蚀和连通性的判断对车辆进行粗定位。实验表明，综合应用这些方法能够取得较为满意的结果。 [2]运动车辆的跟踪。本文提出了一种基于...Traffic incidents detection system plays an important role in the intelligent transportation system. There are many methods to detect the traffic incidents and the video detection method is one of the most rapidly developing technologies in recent years. Due to installation convenience, large detection coverage and high effectiveness, the video detection method is becoming a hotspot in the researc...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机软件与理论学号：20042802

电子警察系统中背景更新算法的研究

摘要: 车辆跟踪检测是基于视频的电子警察系统的基础, 车辆跟踪的典型方法是背景差影法。为了提高检测的准确性,文章提出了一种基于车辆位置信息和帧差法相结合的分块区域动态背景更新算法。该算法将背景更新和车辆检测结合起来, 能准确地分割图像, 具有很好的鲁棒性和准确性, 容易实现, 实时性高。基金项目: 福建省青年科技人才创新项目(2001J003

一种基于位置预判和模板匹配的车辆跟踪算法

本文将位置预判和模板匹配的技术应用于车辆检测与跟踪,构建了一个运动车辆跟踪算法。在对车辆跟踪过程中出现的车辆遮挡、分裂等情况,运用模板匹配技术,取得了较好的结果。实验结果表明,该算法能够对多个车辆进行同时跟踪,同时有较高的实时性

论市政工程项目管理的创新与实践

市政工程项目的管理是市政施工企业的重要前提,主要包括城市道路、桥梁、排水、污水处理、绿化等城市公用事业工程,这些都是城市居民生存与发展过程中不可或缺的物质基础。我国市政工程项目管理已经经过了二十多年的发展,形成了具有现实意义的管理机制,取得了很大的管理成果,但是在实践当中,依然存在着一些问题,需要我们采用科学、合理的解决方法,来实现对市政工程项目管理的创新与发展。</jats:p

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

Determination of the number of ψ(3686) events taken at BESIII

The number of ψ(3686) events collected by the BESIII detector during the 2021 run period is determined to be (2259.3±11.1)×106 by counting inclusive ψ(3686) hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of ψ(3686) events collected during the 2009 and 2012 run periods are updated to be (107.7±0.6)×106 and (345.4±2.6)×106, respectively. Both numbers are consistent with the previous measurements within one standard deviation. The total number of ψ(3686) events in the three data samples is (2712.4±14.3)×10^

Measurement of integrated luminosity of data collected at 3.773 GeV by BESIII from 2021 to 2024

We present a measurement of the integrated luminosity e+e- of collision data collected by the BESIII detector at the BEPCII collider at a center-of-mass energy of Ecm = 3.773 GeV. The integrated luminosities of the datasets taken from December 2021 to June 2022, from November 2022 to June 2023, and from October 2023 to February 2024 were determined to be 4.995±0.019 fb-1, 8.157±0.031 fb-1, and 4.191±0.016 fb-1, respectively, by analyzing large angle Bhabha scattering events. The uncertainties are dominated by systematic effects, and the statistical uncertainties are negligible. Our results provide essential input for future analyses and precision measurements