From 253c8a05c815fd393cebf171259c51590a7609f2 Mon Sep 17 00:00:00 2001 From: JYChen Date: Thu, 24 Mar 2022 18:53:57 +0800 Subject: [PATCH] add Doc for action (#5449) * add Doc for action * update docs --- deploy/pphuman/README.md | 2 +- deploy/pphuman/docs/action.md | 76 ++++++++++++++++++++++++++ deploy/pphuman/docs/images/action.gif | Bin 0 -> 3953776 bytes 3 files changed, 77 insertions(+), 1 deletion(-) create mode 100644 deploy/pphuman/docs/action.md create mode 100644 deploy/pphuman/docs/images/action.gif diff --git a/deploy/pphuman/README.md b/deploy/pphuman/README.md index e9a9e466e..2929eb6f1 100644 --- a/deploy/pphuman/README.md +++ b/deploy/pphuman/README.md @@ -152,5 +152,5 @@ PP-Human整体方案如下图所示 ### 5. 行为识别: - 使用PP-YOLOE + Bytetrack跟踪人体 - 使用HRNet进行关键点检测得到人体17个骨骼点 -- 结合100帧内同一个人骨骼点的变化,通过ST-GCN判断100帧内发生的动作是否为摔倒 +- 结合50帧内同一个人骨骼点的变化,通过ST-GCN判断50帧内发生的动作是否为摔倒 - 详细文档参考[行为识别](docs/action.md) diff --git a/deploy/pphuman/docs/action.md b/deploy/pphuman/docs/action.md new file mode 100644 index 000000000..3417ace00 --- /dev/null +++ b/deploy/pphuman/docs/action.md @@ -0,0 +1,76 @@ +# PP-Human行为识别模块 + +行为识别在智慧社区,安防监控等方向具有广泛应用,PP-Human中集成了基于骨骼点的行为识别模块。 + +
+ +
数据来源及版权归属:天覆科技,感谢提供并开源实际场景数据,仅限学术研究使用
+
+ +## 模型库 +在这里,我们提供了检测/跟踪、关键点识别以及识别摔倒动作的预训练模型,用户可以直接下载使用。 + +| 任务 | 算法 | 精度 | 预测速度(ms) | 下载链接 | +|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: | +| 行人检测/跟踪 | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | 检测: 28ms
跟踪:33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | +| 关键点识别 | HRNet | AP: 87.1 | 单人 2.9ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)| +| 行为识别 | ST-GCN | 准确率: 96.43 | 单人 2.7ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | + + +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。 +4. 预测速度为NVIDIA T4 机器上使用TensorRT FP16时的速度。 + +## 配置说明 +[配置文件](../config/infer_cfg.yml)中与行为识别相关的参数如下: +``` +ACTION: + model_dir: output_inference/STGCN # 模型所在路径 + batch_size: 1 # 预测批大小。 当前仅支持为1进行推理 + max_frames: 50 # 动作片段对应的帧数。在行人ID对应时序骨骼点结果时达到该帧数后,会通过行为识别模型判断该段序列的动作类型。与训练设置一致时效果最佳。 + display_frames: 80 # 显示帧数。当预测结果为摔倒时,在对应人物ID中显示状态的持续时间。 + coord_size: [384, 512] # 坐标统一缩放到的尺度大小。与训练设置一致时效果最佳。 +``` + +## 使用方法 +1. 从上表链接中下载模型并解压到```./output_inference```路径下。 +2. 目前行为识别模块仅支持视频输入,启动命令如下: +```python +python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \ + --video_file=test_video.mp4 \ + --device=gpu \ + --enable_action=True +``` +3. 若修改模型路径,有以下两种方式: + + - ```./deploy/pphuman/config/infer_cfg.yml```下可以配置不同模型路径,关键点模型和行为识别模型分别对应`KPT`和`ACTION`字段,修改对应字段下的路径为实际期望的路径即可。 + - 命令行中增加`--model_dir`修改模型路径: +```python +python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \ + --video_file=test_video.mp4 \ + --device=gpu \ + --enable_action=True \ + --model_dir kpt=./dark_hrnet_w32_256x192 action=./STGCN +``` + +## 方案说明 +1. 使用目标检测与多目标跟踪获取视频输入中的行人检测框及跟踪ID序号,模型方案为PP-YOLOE,详细文档参考[PP-YOLOE](../../../configs/ppyoloe)。 +2. 通过行人检测框的坐标在输入视频的对应帧中截取每个行人,并使用[关键点识别模型](../../../configs/keypoint/hrnet/dark_hrnet_w32_256x192.yml)得到对应的17个骨骼特征点。骨骼特征点的顺序及类型与COCO一致,详见[如何准备关键点数据集](../../../docs/tutorials/PrepareKeypointDataSet_cn.md)中的`COCO数据集`部分。 +3. 每个跟踪ID对应的目标行人各自累计骨骼特征点结果,组成该人物的时序关键点序列。当累计到预定帧数或跟踪丢失后,使用行为识别模型判断时序关键点序列的动作类型。当前版本模型支持摔倒行为的识别,预测得到的`class id`对应关系为: +``` +0: 摔倒, +1: 其他 +``` +4. 行为识别模型使用了[ST-GCN](https://arxiv.org/abs/1801.07455),并基于[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md)套件完成模型训练。 + +## 参考文献 +``` +@inproceedings{stgcn2018aaai, + title = {Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition}, + author = {Sijie Yan and Yuanjun Xiong and Dahua Lin}, + booktitle = {AAAI}, + year = {2018}, +} +``` diff --git a/deploy/pphuman/docs/images/action.gif b/deploy/pphuman/docs/images/action.gif new file mode 100644 index 0000000000000000000000000000000000000000..f69725246b412661f45afac7af4abff882b58c77 GIT binary patch literal 3953776 zcmWieWmpr8*N4|^Fp%!pMt3*jMvm?d$LN$61aX7W4Wks0ZWL4$6m@iW2nY&D2?kgX zCh!0Kc0Qczd_3n|_wP0~G1AcVB7p*c;THh(1%suby`4VJ+E7VO0Sp8H0O+3(2ncWi 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