From 9f75252ea6cf41d82862ff5f30e72374e0bcbf47 Mon Sep 17 00:00:00 2001 From: cuicheng01 Date: Mon, 23 May 2022 07:56:12 +0000 Subject: [PATCH] add Personnel_Access_Demo --- docs/zh_CN/samples/.gitkeep | 1 - docs/zh_CN/samples/Personnel_Access/README.md | 16 ++++++++++++++++ .../samples/Personnel_Access/imgs/someone.gif | Bin 0 -> 752025 bytes 3 files changed, 16 insertions(+), 1 deletion(-) delete mode 100644 docs/zh_CN/samples/.gitkeep create mode 100644 docs/zh_CN/samples/Personnel_Access/README.md create mode 100644 docs/zh_CN/samples/Personnel_Access/imgs/someone.gif diff --git a/docs/zh_CN/samples/.gitkeep b/docs/zh_CN/samples/.gitkeep deleted file mode 100644 index 8b137891..00000000 --- a/docs/zh_CN/samples/.gitkeep +++ /dev/null @@ -1 +0,0 @@ - diff --git a/docs/zh_CN/samples/Personnel_Access/README.md b/docs/zh_CN/samples/Personnel_Access/README.md new file mode 100644 index 00000000..61eec4ed --- /dev/null +++ b/docs/zh_CN/samples/Personnel_Access/README.md @@ -0,0 +1,16 @@ +## 人员出入管理 + +近几年,AI视觉技术在安防、工业制造等场景在产业智能化升级进程中发挥着举足轻重的作用。【进出管控】作为各行业中的关键场景,应用需求十分迫切。 如在居家防盗、机房管控以及景区危险告警等场景中,存在大量对异常目标(人、车或其他物体)不经允许擅自进入规定区域的及时检测需求。利用深度学习视觉技术,可以及时准确地对闯入行为进行识别并发出告警信息。切实保障人员的生命财产安全。相比传统人力监管的方式,不仅可以实现7*24小时不间断的全方位保护,还能极大地降低管理成本,解放劳动力。 + +但在真实产业中,要实现高精度的人员进出识别不是一件容易的事,在实际场景中存在着各种各样的问题: + +**摄像头采集到的图像会受到建筑、机器、车辆等遮挡的影响** + +**天气多种多样,要适应白天、黑夜、雾天和雨天等** + +针对上述场景,本次飞桨产业实践范例库推出了重点区域人员进出管控实践示例,提供从数据准备、技术方案、模型训练优化,到模型部署的全流程可复用方案,有效解决了不同光照、不同天气等室外复杂环境下的图像分类问题,并且极大地降低了数据标注和算力成本,适用于厂区巡检、家居防盗、景区管理等多个产业应用。 + + +![result](./imgs/someone.gif) + +**注**: AI Studio在线运行代码请参考[人员出入管理](https://aistudio.baidu.com/aistudio/projectdetail/4037898) diff --git a/docs/zh_CN/samples/Personnel_Access/imgs/someone.gif b/docs/zh_CN/samples/Personnel_Access/imgs/someone.gif new file mode 100644 index 0000000000000000000000000000000000000000..1f5d684e5455971a636f70540216366166d8d9f8 GIT binary patch literal 752025 zcmV)DK*7I9Nk%w1VJ-w>0{8g<3k(eh1_%-n4h;?x4i6a;4igd%8x9Z^4iOp?6BQa6 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