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[简体中文](README.md) | English
<img src="https://user-images.githubusercontent.com/48054808/185032511-0c97b21c-8bab-4ab1-89ee-16e5e81c22cc.png" title="" alt="" data-align="center">
**PaddleDetection has provide out-of-the-box tools in pedestrian and vehicle analysis, and it support multiple input format such as images/videos/multi-videos/online video streams. This make it popular in smart-city\smart transportation and so on. It can be deployed easily with GPU server and TensorRT, which achieves real-time performace.**
- 🚶‍♂️🚶‍♀️ **PP-Human has four major toolbox for pedestrian analysis: five example of behavior analysis、26 attributes recognition、in-out counting、multi-target-multi-camera tracking(REID).**
- 🚗🚙 **PP-Vehicle has four major toolbox for vehicle analysis: The license plate recognition、vechile attributes、in-out counting、illegal_parking recognition.**
![](https://user-images.githubusercontent.com/22989727/202134414-713a00d6-a0a4-4a77-b6e8-05cdb5d42b1e.gif)
## 📣 Updates
- 🔥🔥🔥 PP-YOLOE-PLUS-Tiny was launched for Jetson deploy, which has achieved 20fps while four rtsp streams work at the same time; PP-Vehicle was launched with retrograde and lane line press.
- 🔥 **2022.8.20：PP-Vehicle was first launched with four major toolbox for vehicle analysis，and it also provide detailed documentation for user to train with their own datas and model optimize.**
- 🔥 2022.7.13：PP-Human v2 launched with a full upgrade of four industrial features: behavior analysis, attributes recognition, visitor traffic statistics and ReID. It provides a strong core algorithm for pedestrian detection, tracking and attribute analysis with a simple and detailed development process and model optimization strategy.
- 2022.4.18: Add PP-Human practical tutorials, including training, deployment, and action expansion. Details for AIStudio project please see [Link](https://aistudio.baidu.com/aistudio/projectdetail/3842982)
- 2022.4.10: Add PP-Human examples; empower refined management of intelligent community management. A quick start for AIStudio [Link](https://aistudio.baidu.com/aistudio/projectdetail/3679564)
- 2022.4.5: Launch the real-time pedestrian analysis tool PP-Human. It supports pedestrian tracking, visitor traffic statistics, attributes recognition, and falling detection. Due to its specific optimization of real-scene data, it can accurately recognize various falling gestures, and adapt to different environmental backgrounds, light and camera angles.
![](https://user-images.githubusercontent.com/48054808/184843170-c3ef7d29-913b-4c6e-b533-b83892a8b0e2.gif)
## 🔮 Features and demonstration
### PP-Human
| ⭐ Feature | 💟 Advantages | 💡Example |
| -------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| **ReID** | Extraordinary performance: special optimization for technical challenges such as target occlusion, uncompleted and blurry objects to achieve mAP 98.8, 1.5ms/person | <img src="https://user-images.githubusercontent.com/48054808/173037607-0a5deadc-076e-4dcc-bd96-d54eea205f1f.png" title="" alt="" width="191"> |
| **Attribute analysis** | Compatible with a variety of data formats: support for images, video input<br/><br/>High performance: Integrated open-sourced datasets with real enterprise data for training, achieved mAP 94.86, 2ms/person<br/><br/>Support 26 attributes: gender, age, glasses, tops, shoes, hats, backpacks and other 26 high-frequency attributes | <img src="https://user-images.githubusercontent.com/48054808/173036043-68b90df7-e95e-4ada-96ae-20f52bc98d7c.png" title="" alt="" width="207"> |
| **Behaviour detection** | Rich function: support five high-frequency anomaly behavior detection of falling, fighting, smoking, telephoning, and intrusion<br/><br/>Robust: unlimited by different environmental backgrounds, light, and camera angles.<br/><br/>High performance: Compared with video recognition technology, it takes significantly smaller computation resources; support localization and service-oriented rapid deployment<br/><br/>Fast training: only takes 15 minutes to produce high precision behavior detection models | <img src="https://user-images.githubusercontent.com/48054808/173034825-623e4f78-22a5-4f14-9b83-dc47aa868478.gif" title="" alt="" width="209"> |
| **Visitor traffic statistics**<br>**Trace record** | Simple and easy to use: single parameter to initiate functions of visitor traffic statistics and trace record | <img src="https://user-images.githubusercontent.com/22989727/174736440-87cd5169-c939-48f8-90a1-0495a1fcb2b1.gif" title="" alt="" width="200"> |
### PP-Vehicle
| ⭐ Feature | 💟 Advantages | 💡 Example |
| ---------- | ------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- |
| **License Plate-Recognition** | Both support for traditional plate and new green plate <br/><br/> Sample the frame in a time windows to recognice the plate license, and vots the license in many samples, which lead less compute cost and better accuracy, and the result is much more stable. <br/><br/> hmean of text detector: 0.979; accuracy of recognition model: 0.773 <br/><br/> | <img title="" src="https://user-images.githubusercontent.com/48054808/185027987-6144cafd-0286-4c32-8425-7ab9515d1ec3.png" alt="" width="191"> |
| **Vehicle Attributes** | Identify 10 vehicle colors and 9 models <br/><br/> More powerfull backbone: PP-HGNet/PP-LCNet, with higher accuracy and faster speed <br/><br/> accuracy of model: 90.81 <br/><br/> | <img title="" src="https://user-images.githubusercontent.com/48054808/185044490-00edd930-1885-4e79-b3d4-3a39a77dea93.gif" alt="" width="207"> |
| **Illegal Parking** | Easy to use with one line command, and define the illegal area by yourself <br/><br/> Get the license of illegal car <br/><br/> | <img title="" src="https://user-images.githubusercontent.com/48054808/185028419-58ae0af8-a035-42e7-9583-25f5e4ce0169.png" alt="" width="209"> |
| **in-out counting** | Easy to use with one line command, and define the in-out line by yourself <br/><br/> Target route visualize with high tracking performance | <img title="" src="https://user-images.githubusercontent.com/48054808/185028798-9e07379f-7486-4266-9d27-3aec943593e0.gif" alt="" width="200"> |
| **vehicle retrograde** | Easy to use with one line command <br/><br/> High precision Segmetation model PP-LiteSeg | <img title="" src="https://raw.githubusercontent.com/LokeZhou/PaddleDetection/develop/deploy/pipeline/docs/images/vehicle_retrograde.gif" alt="" width="200"> |
| **vehicle press line** | Easy to use with one line command <br/><br/> High precision Segmetation model PP-LiteSeg | <img title="" src="https://raw.githubusercontent.com/LokeZhou/PaddleDetection/develop/deploy/pipeline/docs/images/vehicle_press.gif" alt="" width="200"> |
## 🗳 Model Zoo
<details>
<summary><b>PP-Human End-to-end model results (click to expand)</b></summary>
| Task | End-to-End Speed（ms） | Model | Size |
|:--------------------------------------:|:--------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|
| Pedestrian detection (high precision) | 25.1ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M |
| Pedestrian detection (lightweight) | 16.2ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M |
| Pedestrian detection (super lightweight) | 10ms(Jetson AGX) | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.tar.gz) | 17M |
| Pedestrian tracking (high precision) | 31.8ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M |
| Pedestrian tracking (lightweight) | 21.0ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M |
| Pedestrian tracking（super lightweight） | 13.2ms(Jetson AGX) | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.tar.gz) | 17M |
| MTMCT(REID) | Single Person 1.5ms | [REID](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) | REID：92M |
| Attribute recognition (high precision) | Single person8.5ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br> [Attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | Object detection：182M<br>Attribute recognition：86M |
| Attribute recognition (lightweight) | Single person 7.1ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br> [Attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | Object detection：182M<br>Attribute recognition：86M |
| Falling detection | Single person 10ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) <br> [Keypoint detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) <br> [Behavior detection based on key points](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | Multi-object tracking：182M<br>Keypoint detection：101M<br>Behavior detection based on key points: 21.8M |
| Intrusion detection | 31.8ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M |
| Fighting detection | 19.7ms | [Video classification](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 90M |
| Smoking detection | Single person 15.1ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br>[Object detection based on Human Id](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | Object detection：182M<br>Object detection based on Human ID: 27M |
| Phoning detection | Single person ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br>[Image classification based on Human ID](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | Object detection：182M<br>Image classification based on Human ID：45M |
</details>
<details>
<summary><b>PP-Vehicle End-to-end model results (click to expand)</b></summary>
| Task | End-to-End Speed（ms） | Model | Size |
|:--------------------------------------:|:--------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|
| Vehicle detection (high precision) | 25.7ms | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M |
| Vehicle detection (lightweight) | 13.2ms | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M |
| Vehicle detection (super lightweight) | 10ms(Jetson AGX) | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppvehicle/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.tar.gz) | 17M |
| Vehicle tracking (high precision) | 40ms | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M |
| Vehicle tracking (lightweight) | 25ms | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M |
| Vehicle tracking (super lightweight) | 13.2ms(Jetson AGX) | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppvehicle/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.tar.gz) | 17M |
| Plate Recognition | 4.68ms | [plate detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)<br>[plate recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | Plate detection：3.9M<br>Plate recognition：12M |
| Vehicle attribute | 7.31ms | [attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M |
| Lane line Segmentation | 47ms | [Lane line Segmentation](https://bj.bcebos.com/v1/paddledet/models/pipeline/pp_lite_stdc2_bdd100k.zip) | 47M |
</details>
Click to download the model, then unzip and save it in the `. /output_inference`.
## 📚 Doc Tutorials
### 🚶‍♀️ PP-Human
#### [A Quick Start](docs/tutorials/PPHuman_QUICK_STARTED_en.md)
#### Pedestrian attribute/feature recognition
* [A quick start](docs/tutorials/pphuman_attribute_en.md)
* [Customized development tutorials](../../docs/advanced_tutorials/customization/pphuman_attribute_en.md)
#### Behavior detection
* [A quick start](docs/tutorials/pphuman_action_en.md)
* [Customized development tutorials](../../docs/advanced_tutorials/customization/action_recognotion/README_en.md)
#### ReID
* [A quick start](docs/tutorials/pphuman_mtmct_en.md)
* [Customized development tutorials](../../docs/advanced_tutorials/customization/pphuman_mtmct_en.md)
#### Pedestrian tracking, visitor traffic statistics, trace records
* [A quick start](docs/tutorials/pphuman_mot_en.md)
* [Customized development tutorials](../../docs/advanced_tutorials/customization/pphuman_mot_en.md)
### 🚘 PP-Vehicle
#### [A Quick Start](docs/tutorials/PPVehicle_QUICK_STARTED.md)
#### Vehicle Plate License
- [A quick start](docs/tutorials/ppvehicle_plate_en.md)
- [Customized development tutorials](../../docs/advanced_tutorials/customization/ppvehicle_plate.md)
#### Vehicle Attributes
- [A quick start](docs/tutorials/ppvehicle_attribute_en.md)
- [Customized development tutorials](../../docs/advanced_tutorials/customization/ppvehicle_attribute_en.md)
#### Illegal Parking
- [A quick start](docs/tutorials/ppvehicle_illegal_parking_en.md)
- [Customized development tutorials](../../docs/advanced_tutorials/customization/pphuman_mot_en.md)
#### Vehicle Tracking/in-out counint/Route Visualize
- [A quick start](docs/tutorials/ppvehicle_mot_en.md)
- [Customized development tutorials](../../docs/advanced_tutorials/customization/pphuman_mot_en.md)
#### Vehicle Press Line
- [A quick start](docs/tutorials/ppvehicle_press_en.md)
- [Customized development tutorials](../../docs/advanced_tutorials/customization/ppvehicle_violation_en.md)
#### Vehicle Retrograde
- [A quick start](docs/tutorials/ppvehicle_retrograde_en.md)
- [Customized development tutorials](../../docs/advanced_tutorials/customization/ppvehicle_violation_en.md)