@@ -60,9 +60,9 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model
## How to Use
- Download models from the links of the above table and unzip them to ```./output_inference```.
1. Download models `Pedestrian Detection/Tracking`, `Keypoint Detection` and `Falling Recognition` from the links in the Model Zoo and unzip them to ```./output_inference```. The models are automatically downloaded by default. If you download them manually, you need to modify the `model_dir` as the model storage path.
- Now the only available input is the video input in the action recognition module. set the "enable: True" of `SKELETON_ACTION` in infer_cfg_pphuman.yml. And then run the command:
2. Now the only available input is the video input in the action recognition module. set the "enable: True" of `SKELETON_ACTION` in infer_cfg_pphuman.yml. And then run the command:
@@ -70,7 +70,7 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model
--device=gpu
```
- There are two ways to modify the model path:
3. There are two ways to modify the model path:
- In ```./deploy/pipeline/config/infer_cfg_pphuman.yml```, you can configurate different model paths，which is proper only if you match keypoint models and action recognition models with the fields of `KPT` and `SKELETON_ACTION` respectively, and modify the corresponding path of each field into the expected path.
- Add `--model_dir` in the command line to revise the model path：
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@@ -81,6 +81,7 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model
4. For detailed parameter description, please refer to [Parameter Description](./QUICK_STARTED.md)
### Introduction to the Solution
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@@ -98,7 +99,7 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model
```
- The falling action recognition model uses [ST-GCN](https://arxiv.org/abs/1801.07455), and employ the [PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md) toolkit to complete model training.
<divalign="center"><imgsrc="../images/calling.gif"width='1000'/><center>Data source and copyright owner：Skyinfor
Technology. Thanks for the provision of actual scenario data, which are only
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@@ -122,9 +123,9 @@ ID_BASED_CLSACTION: # config for classfication-based action recognition model
### How to Use
1. Download models from the links of the above table and unzip them to ```./output_inference```.
1. Download models `Pedestrian Detection/Tracking` and `Calling Recognition` from the links in `Model Zoo` and unzip them to ```./output_inference```. The models are automatically downloaded by default. If you download them manually, you need to modify the `model_dir` as the model storage path.
2. Now the only available input is the video input in the action recognition module. set the "enable: True" of `ID_BASED_CLSACTION` in infer_cfg_pphuman.yml.
2. Now the only available input is the video input in the action recognition module. Set the "enable: True" of `ID_BASED_CLSACTION` in infer_cfg_pphuman.yml.
3. Run this command:
```python
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@@ -132,6 +133,7 @@ ID_BASED_CLSACTION: # config for classfication-based action recognition model
--video_file=test_video.mp4 \
--device=gpu
```
4. For detailed parameter description, please refer to [Parameter Description](./QUICK_STARTED.md)
### Introduction to the Solution
1. Get the pedestrian detection box and the tracking ID number of the video input through object detection and multi-object tracking. The adopted model is PP-YOLOE, and for details, please refer to [PP-YOLOE](../../../configs/ppyoloe).
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@@ -168,7 +170,7 @@ ID_BASED_DETACTION: # Config for detection-based action recognition model
### How to Use
1. Download models from the links of the above table and unzip them to ```./output_inference```.
1. Download models `Pedestrian Detection/Tracking` and `Smoking Recognition` from the links in `Model Zoo` and unzip them to ```./output_inference```. The models are automatically downloaded by default. If you download them manually, you need to modify the `model_dir` as the model storage path.
2. Now the only available input is the video input in the action recognition module. set the "enable: True" of `ID_BASED_DETACTION` in infer_cfg_pphuman.yml.
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@@ -178,6 +180,7 @@ ID_BASED_DETACTION: # Config for detection-based action recognition model
--video_file=test_video.mp4 \
--device=gpu
```
4. For detailed parameter description, please refer to [Parameter Description](./QUICK_STARTED.md)
### Introduction to the Solution
1. Get the pedestrian detection box and the tracking ID number of the video input through object detection and multi-object tracking. The adopted model is PP-YOLOE, and for details, please refer to [PP-YOLOE](../../../../configs/ppyoloe).
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@@ -223,18 +226,20 @@ VIDEO_ACTION: # Config for detection-based action recognition model
### How to Use
1. Download models from the links of the above table and unzip them to ```./output_inference```.
1. Download model`Fighting Detection` from the links of the above table and unzip it to ```./output_inference```. The models are automatically downloaded by default. If you download them manually, you need to modify the `model_dir` as the model storage path.
2. Modify the file names in the `ppTSM` folder to `model.pdiparams, model.pdiparams.info and model.pdmodel`;
3. Now the only available input is the video input in the action recognition module. set the "enable: True" of `VIDEO_ACTION` in infer_cfg_pphuman.yml.
5. For detailed parameter description, please refer to [Parameter Description](./QUICK_STARTED.md).
The result is shown as follow:
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@@ -252,14 +257,14 @@ The current fight recognition model is using [PP-TSM](https://github.com/PaddleP
The pretrained models are provided and can be used directly, including pedestrian detection/ tracking, keypoint detection, smoking, calling and fighting recognition. If users need to train custom action or optimize the model performance, please refer the link below.
mkdir{root of PaddleVideo}/applications/PPHuman/datasets/annotations
mv det_keypoint_unite_image_results.json {root of PaddleVideo}/applications/PPHuman/datasets/annotations/det_keypoint_unite_image_results_{video_id}_{camera_id}.json
cd{root of PaddleVideo}/applications/PPHuman/datasets/
