- 1、本文档共51页,可阅读全部内容。
- 2、有哪些信誉好的足球投注网站(book118)网站文档一经付费(服务费),不意味着购买了该文档的版权,仅供个人/单位学习、研究之用,不得用于商业用途,未经授权,严禁复制、发行、汇编、翻译或者网络传播等,侵权必究。
- 3、本站所有内容均由合作方或网友上传,本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺!文档内容仅供研究参考,付费前请自行鉴别。如您付费,意味着您自己接受本站规则且自行承担风险,本站不退款、不进行额外附加服务;查看《如何避免下载的几个坑》。如果您已付费下载过本站文档,您可以点击 这里二次下载。
- 4、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等,请点击“版权申诉”(推荐),也可以打举报电话:400-050-0827(电话支持时间:9:00-18:30)。
查看更多
AbstractWith
Abstract
With the rapid development of video surveillance technology,there are more and more needs for functional and high performance video surveillance products.However, due to long time out of repair,equipment is aging and unstable which causes video suffering noise in different levels of quality degradation.To solve this problem,this article has proposed a deep learning based method to detect leaf occlusion and blur video.This work includes the following aspects:
In the first part,we have reviewed the theory and development of eonvolutional
neural network.nlen the researches of video surveillance distortion detection and the current research status of image quality assessment detection algorithms have been reviewed.At the end of this part,we have summarized the surveillance video distortion types,and specifically analyzed the leaves occlusion and video sharpness assessment
problem.
The objective of leaf occlusion detection in video is to automatically determine whether the video suffers from leaf occlusion or not.To tackle this challenge,this paper proposes a two—step learning framework for’leaf occlusion detection.First,the
convolutional neural network is used to learn the discriminative features of leaf particles.
Then the trmned model is used to detect candidate leaf patches in the image.Second,a probabilistic approach is used to pool decisions of each candidate leaf patch to produce final detection result in the video.To assess the video sharpness,we have first used the ELO rating system to get the ground truth,then tried different convolution neural network structures to get better result to assess sharpness,and introduced the details in implementing project.
Experimental results are provided to demonstrate that the proposed approach can effectively detect the leaf occlusion and assess the quality of sharpness in real—world traffic surveillance video.
Keywords:Deep learning;convolutional neural network;quality assessment;
occlusion detection;sh
您可能关注的文档
- 基于区域纵联比较原理的微电网保护方案-电气工程专业论文.docx
- 基于梯度特征和纹理特征的行人检测-模式识别与智能系统专业论文.docx
- 基于深度相机的三维人体重建及在服装展示方面的技术研究-服装设计与工程专业论文.docx
- 基于声发射的非接触式机械密封膜厚检测技术-测试计量技术及仪器专业论文.docx
- 基于六自由度机器人的四工位砂带磨削系统开发-机械电子工程专业论文.docx
- 基于容积脉搏波梯度法的血压检测系统研制-控制工程专业论文.docx
- 基于内容的多媒体视觉信息有哪些信誉好的足球投注网站研究-信号与信息处理专业论文.docx
- 基于阵列波导光栅(AWG)的便携式拉曼光谱仪的系统研究-测试计量技术与仪器专业论文.docx
- 基于卡尔曼滤波的开采沉陷地表移动变形分析与预报-大地测量学与测量工程专业论文.docx
- 基于口语语料库的英语自我修补性别差异研究-英语语言文学专业论文.docx
- 第十一章 电流和电路专题特训二 实物图与电路图的互画 教学设计 2024-2025学年鲁科版物理九年级上册.docx
- 人教版七年级上册信息技术6.3加工音频素材 教学设计.docx
- 5.1自然地理环境的整体性 说课教案 (1).docx
- 4.1 夯实法治基础 教学设计-2023-2024学年统编版九年级道德与法治上册.docx
- 3.1 光的色彩 颜色 电子教案 2023-2024学年苏科版为了八年级上学期.docx
- 小学体育与健康 四年级下册健康教育 教案.docx
- 2024-2025学年初中数学九年级下册北京课改版(2024)教学设计合集.docx
- 2024-2025学年初中科学七年级下册浙教版(2024)教学设计合集.docx
- 2024-2025学年小学信息技术(信息科技)六年级下册浙摄影版(2013)教学设计合集.docx
- 2024-2025学年小学美术二年级下册人美版(常锐伦、欧京海)教学设计合集.docx
文档评论(0)