From b9b2eef458037214864e3a223f9e7b3519a85a45 Mon Sep 17 00:00:00 2001 From: dancing-ui <2779856074@qq.com> Date: Fri, 1 Nov 2024 11:43:40 +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/2024-11-01/README.md | 25 ++++++++++++++++++ Docs/2024-11-01/image/person_reid_service.png | Bin 0 -> 188895 bytes Docs/2024-11-01/image/search.png | Bin 0 -> 98540 bytes 3 files changed, 25 insertions(+) create mode 100644 Docs/2024-11-01/README.md create mode 100644 Docs/2024-11-01/image/person_reid_service.png create mode 100644 Docs/2024-11-01/image/search.png diff --git a/Docs/2024-11-01/README.md b/Docs/2024-11-01/README.md new file mode 100644 index 0000000..f3fbe0a --- /dev/null +++ b/Docs/2024-11-01/README.md @@ -0,0 +1,25 @@ +# 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) +### 2.2 代码实现行人重识别 +1. 完成Facebook向量数据库Faiss GPU版本(CPU版本完全不能用,换成GPU版本结合批处理之后,效率提升了10倍左右)的接口适配 +2. 完成Facebook键值数据库RocksDB(LevelDB的改进版)的接口适配,基本实现特征向量的数据持久化,存储耗时在当前行人情景下基本可以忽略不计 +3. 基本打通行人重识别服务流程链路,需要进一步优化耗时以及准确率 +### 2.3 待办事项 +- 视频流卡顿问题 + - 当前结果:目前Faiss是对每一张图片都进行行人检索,然后实时更新每一张检索出来的行人特征向量;8张图片完成整个过程,平均下来大概需要60ms左右![alt text](image/search.png) + - 尝试方向 + 1. 考虑是faiss::gpu::GpuIndexFlat暴力匹配太慢了,换成faiss::gpu::GpuIndexIVFPQ利用倒排索引以及乘积量化的方法优化一下应该会更快 + 2. 使用fast-reid提取行人特征时的耗时是最大的,8张图片的情况下,大概需要160ms左右;之前的目标跟踪非常流畅是由于deepsort跟踪算法的耗时几乎可以忽略;然而,在行人重识别服务中,由于需要对每一张图片都进行检索和更新,所以耗时会增加很多;目前针对特征提取这块的优化,还没有头绪 +- ID Switch问题 + - 当前结果:行人重叠或者背景变换较大时,会有ID Switch现象 + - 尝试方向 + 1. 目前由于Faiss的GPU版本没有实现删除特征向量的方法,所以现在基本没有对特征向量进行更新,而是沿用行人第一次出现的特征向量的ID,这也许是导致出现ID Switch现象的原因,考虑实现一个更新特征向量的方法 +- HTTP客户端 + - 目前没有走到发送HTTP消息时,等待解决上述问题之后,再考虑HTTP消息的构造与发送 + +[项目链接](https://github.com/dancing-ui/uestc_vhm) \ No newline at end of file diff --git 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