add dataset links, test=document_fix (#5780)

* add dataset links, test=document_fix

* add ai-studio link, test=document_fix

deploy/pphuman/README.md

@@ -7,6 +7,8 @@ PP-Human是基于飞桨深度学习框架的业界首个开源的实时行人分
 
 PP-Human赋能社区智能精细化管理, AIStudio快速上手教程[链接](https://aistudio.baidu.com/aistudio/projectdetail/3679564)
 
+实时行人分析全流程实战, 覆盖训练、部署、动作类型扩展等内容，AIStudio项目请见[链接](https://aistudio.baidu.com/aistudio/projectdetail/3842982)
+
 ## 一、环境准备
 
 环境要求： PaddleDetection版本 >= release/2.4 或 develop版本

deploy/pphuman/README_en.md

@@ -5,6 +5,10 @@ English | [简体中文](README.md)
 PP-Human serves as the first open-source tool of real-time pedestrian anaylsis relying on the PaddlePaddle deep learning framework. Versatile and efficient in deployment, it has been used in various senarios. PP-Human
 offers many input options, including image/single-camera video/multi-camera video, and covers multi-object tracking, attribute recognition, and action recognition. PP-Human can be applied to intelligent traffic, the intelligent community, industiral patrol, and so on. It supports server-side deployment and TensorRT acceleration，and achieves real-time analysis on the T4 server.
 
+Community intelligent management supportted by PP-Human, please refer to this [AI Studio project](https://aistudio.baidu.com/aistudio/projectdetail/3679564) for quick start tutorial.
+
+Full-process operation tutorial of PP-Human, covering training, deployment, action expansion, please refer to this [AI Studio project](https://aistudio.baidu.com/aistudio/projectdetail/3842982).
+
 ## I. Environment Preparation
 
 Requirement: PaddleDetection version >= release/2.4 or develop

deploy/pphuman/docs/action.md

@@ -18,9 +18,9 @@
 
 
 注：
-1. 检测/跟踪模型精度为MOT17，CrowdHuman,HIEVE和部分业务数据融合训练测试得到。
-2. 关键点模型使用COCO，UAVHuman和部分业务数据融合训练, 精度在业务数据测试集上得到。
-3. 行为识别模型使用NTU-RGB+D，UR Fall Detection Dataset和部分业务数据融合训练，精度在业务数据测试集上得到。
+1. 检测/跟踪模型精度为[MOT17](https://motchallenge.net/)，[CrowdHuman](http://www.crowdhuman.org/)，[HIEVE](http://humaninevents.org/)和部分业务数据融合训练测试得到。
+2. 关键点模型使用[COCO](https://cocodataset.org/)，[UAV-Human](https://github.com/SUTDCV/UAV-Human)和部分业务数据融合训练, 精度在业务数据测试集上得到。
+3. 行为识别模型使用[NTU-RGB+D](https://rose1.ntu.edu.sg/dataset/actionRecognition/)，[UR Fall Detection Dataset](http://fenix.univ.rzeszow.pl/~mkepski/ds/uf.html)和部分业务数据融合训练，精度在业务数据测试集上得到。
 4. 预测速度为NVIDIA T4 机器上使用TensorRT FP16时的速度, 速度包含数据预处理、模型预测、后处理全流程。
 
 ## 配置说明