From 50dd6a0686b67c87d9071bccc6bd7a6de8947b62 Mon Sep 17 00:00:00 2001 From: dancing-ui <2779856074@qq.com> Date: Fri, 25 Oct 2024 10:58:15 +0800 Subject: [PATCH] =?UTF-8?q?[=E6=96=87=E6=A1=A3=E8=A1=A5=E5=85=85]=20?= =?UTF-8?q?=E6=96=B0=E5=A2=9E=E7=BB=84=E4=BC=9A=E6=96=87=E6=A1=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Docs/2024-10-25/README.md | 29 ++++++++++++++++++ Docs/2024-10-25/image/person_reid_service.png | Bin 0 -> 311194 bytes 2 files changed, 29 insertions(+) create mode 100644 Docs/2024-10-25/README.md create mode 100644 Docs/2024-10-25/image/person_reid_service.png diff --git a/Docs/2024-10-25/README.md b/Docs/2024-10-25/README.md new file mode 100644 index 0000000..d1941d1 --- /dev/null +++ b/Docs/2024-10-25/README.md @@ -0,0 +1,29 @@ +# 2024-10-25 组会汇报 + +## 一、科研项目 +- 无新进展 +## 二、工程项目 +### 2.1 梳理行人重识别服务流程 +![alt text](image/person_reid_service.png) +检测到行人之后 +- 首先在数据库里检索是否有相似度比较高的人 + - 这一步需要用向量数据库来做,若出现,则给出对应的id,否则id为无效值 +- 之后给系统后端发送一个http消息,http消息的json格式如下: + ```json + { + "PersonId": 1 // 行人id + "Picture": "xxx"// base64编码后的行人原图数据 + "CameraIP": "192.169.0.0" //摄像头IP地址 + "TimeStamp": "xxx"//时间戳 + } + ``` +- 判定这段视频里后面几秒内是否重复出现该行人,注意以下事项: + - 视频可见范围内,只发一次http消息 + - 相似度高的人不管在摄像头中消失多少次,都是一个相同的ID +### 2.2 熟悉faiss等第三方库的使用 +- Dockerfile配置:源码编译安装faiss、leveldb、libhv等第三方依赖库,解决依赖库之间的版本兼容问题(Cuda,CMake,Docker镜像相关) +- Demo示例学习:查阅官方说明文档,学习faiss和leveldb的插入和检索操作,学习如何使用libhv实现一个简单的http客户端发送http消息 +### 2.3 待办事项 +- 按照前文的流程图,代码实现行人重识别服务 + +[项目链接](https://github.com/dancing-ui/uestc_vhm) \ No newline at end of file diff --git a/Docs/2024-10-25/image/person_reid_service.png b/Docs/2024-10-25/image/person_reid_service.png new file mode 100644 index 0000000000000000000000000000000000000000..bf6ccd9d22bd973fb4f980618c5c3d7137a87d2d GIT binary patch literal 311194 zcmeEuWmJ@F)V3ffpdyNhARvl}k_r+IDGCTEAsquKC5?2ah)OB~0us_SL)Q$YNJvWO z&^a)JKuYrhM2__J 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