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 even can achieve real-time analysis on the T4 server.
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.
## I. Environment Preparation
Requirement: PaddleDetection version >= release/2.4
Requirement: PaddleDetection version >= release/2.4 or develop
The installation of PaddlePaddle and PaddleDetection
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@@ -38,18 +38,19 @@ To make users have access to models of different scenarios, PP-Human provides pr
| Task | Scenario | Precision | Inference Speed（FPS） | Model Inference and Deployment |
Then, unzip the downloaded model to the folder `./output_inference`.
**Note: **
- The model precision is decided by the fusion of datasets which include open-source datasets and enterprise ones.
- When the inference speed is T4, use TensorRT FP16.
- The precision on ReID model is evaluated on Market1501.
- The inference speed is tested on T4, using TensorRT FP16. The pipeline of preprocess, prediction and postprocess is included.
### 2. Preparation of Configuration Files
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@@ -80,31 +81,44 @@ ATTR:
batch_size: 8
```
**Note: **
- For different tasks, users could add `--enable_attr=True` or `--enable_action=True` in command line and do not need to set config file.
- if only need to change the model path, users could add `--model_dir det=ppyoloe/` in command line and do not need to set config file. For details info please refer to doc below.
### 3. Inference and Deployment
```
# Specify the config file path and test images
# Pedestrian detection. Specify the config file path and test images
Other usage please refer to [sub-task docs](./docs)
### 3.1 Description of Parameters
| Parameter | Optional or not| Meaning |
|-------|-------|----------|
| --config | Yes | Config file path |
| --model_dir | Option | the model paths of different tasks in PP-Human, with a priority higher than config files |
| --model_dir | Option | the model paths of different tasks in PP-Human, with a priority higher than config files. For example, `--model_dir det=better_det/ attr=better_attr/` |
@@ -130,21 +144,21 @@ The overall solution of PP-Human is as follows:
### 1. Object Detection
- Use PP-YOLOE L as the model of object detection
- For details, please refer to [PP-YOLOE](../../configs/ppyoloe/)
- For details, please refer to [PP-YOLOE](../../configs/ppyoloe/) and [Detection and Tracking](docs/mot_en.md)
### 2. Multi-Object Tracking
- Conduct multi-object tracking with the SDE solution
- Use PP-YOLOE L as the detection model
- Use the Bytetrack solution to track modules
- For details, refer to [Bytetrack](configs/mot/bytetrack)
- For details, refer to [Bytetrack](configs/mot/bytetrack) and [Detection and Tracking](docs/mot_en.md)
### 3. Cross-Camera Tracking
### 3. Multi-Camera Tracking
- Use PP-YOLOE + Bytetrack to obtain the tracks of single-camera multi-object tracking
- Use ReID（centroid network）to extract features of the detection result of each frame
- Match the features of multi-camera tracks to get the cross-camera tracking result
- For details, please refer to [Cross-Camera Tracking](docs/mtmct_en.md)
- For details, please refer to [Multi-Camera Tracking](docs/mtmct_en.md)
### 4. Multi-Target Multi-Camera Tracking
### 4. Attribute Recognition
- Use PP-YOLOE + Bytetrack to track humans
- Use StrongBaseline（a multi-class model）to conduct attribute recognition, and the main attributes include age, gender, hats, eyes, clothing, and backpacks.
- For details, please refer to [Attribute Recognition](docs/attribute_en.md)
