**PP-Human is the industry's first open-sourced real-time pedestrian analysis tool based on PaddlePaddle deep learning framework. It has three major features: rich functions, wide application, and efficient deployment.**
**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.**
PP-Human supports various inputs such as images, single-camera, and multi-camera videos. It covers multi-object tracking, attributes recognition, behavior analysis, visitor traffic statistics, and trace records. PP-Human can be applied to fields including Smart Transportation, Smart Community, and industrial inspections. It can also be deployed on server sides and TensorRT accelerator. On the T4 server, it could achieve real-time analysis.
- 🔥 **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.**
- 🔥🔥🔥 **2022.8.20：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.
| **ReID** | Extraordinary performance: special optimization for technical challenges such as target occlusion, uncompleted and blurry objects to achieve mAP 98.8, 1.5ms/person | <imgsrc="https://user-images.githubusercontent.com/48054808/173037607-0a5deadc-076e-4dcc-bd96-d54eea205f1f.png"title=""alt=""width="191"> |
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@@ -29,6 +33,16 @@ PP-Human supports various inputs such as images, single-camera, and multi-camera
| **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 | <imgsrc="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 | <imgsrc="https://user-images.githubusercontent.com/22989727/174736440-87cd5169-c939-48f8-90a1-0495a1fcb2b1.gif"title=""alt=""width="200"> |
| **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/> | <imgtitle=""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/> | <imgtitle=""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/> | <imgtitle=""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 | <imgtitle=""src="https://user-images.githubusercontent.com/48054808/185028798-9e07379f-7486-4266-9d27-3aec943593e0.gif"alt=""width="200"> |
## 🗳 Model Zoo
<details>
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@@ -40,6 +54,7 @@ PP-Human supports various inputs such as images, single-camera, and multi-camera
| 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 |
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@@ -69,40 +84,60 @@ Click to download the model, then unzip and save it in the `. /output_inference`
| Single-camera video | Attribute Recognition | Multi-Object Tracking Attribute Recognition | MOT ATTR |
| Single-camera video | Attribute Recognition | Multi-Object Tracking Attribute Recognition | MOT VEHICLEPLATE |
| Single-camera video | License-plate Recognition | Multi-Object Tracking License-plate Recognition | MOT VEHICLEPLATE |
Take attribute recognition based on video input as an example: Its task type includes multi-object tracking and attributes recognition. The specific configuration is as follows.
