README_en.md 21 KB
简体中文 | English
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.
📣 Updates
- 🔥🔥🔥 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
2022.4.10: Add PP-Human examples; empower refined management of intelligent community management. A quick start for AIStudio Link
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.
🔮 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 |  |
| Attribute analysis | Compatible with a variety of data formats: support for images, video input High performance: Integrated open-sourced datasets with real enterprise data for training, achieved mAP 94.86, 2ms/person Support 26 attributes: gender, age, glasses, tops, shoes, hats, backpacks and other 26 high-frequency attributes |
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| Behaviour detection | Rich function: support five high-frequency anomaly behavior detection of falling, fighting, smoking, telephoning, and intrusion Robust: unlimited by different environmental backgrounds, light, and camera angles. High performance: Compared with video recognition technology, it takes significantly smaller computation resources; support localization and service-oriented rapid deployment Fast training: only takes 15 minutes to produce high precision behavior detection models |
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| Visitor traffic statistics Trace record |
Simple and easy to use: single parameter to initiate functions of visitor traffic statistics and trace record |  |
PP-Vehicle
| ⭐ Feature | 💟 Advantages | 💡 Example |
|---|---|---|
| License Plate-Recognition | Both support for traditional plate and new green plate 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. hmean of text detector: 0.979; accuracy of recognition model: 0.773 |
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| Vehicle Attributes | Identify 10 vehicle colors and 9 models More powerfull backbone: PP-HGNet/PP-LCNet, with higher accuracy and faster speed accuracy of model: 90.81 |
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| Illegal Parking | Easy to use with one line command, and define the illegal area by yourself Get the license of illegal car |
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| in-out counting | Easy to use with one line command, and define the in-out line by yourself Target route visualize with high tracking performance |
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🗳 Model Zoo
PP-Human End-to-end model results (click to expand)
| Task | End-to-End Speed（ms） | Model | Size |
|---|---|---|---|
| Pedestrian detection (high precision) | 25.1ms | Multi-object tracking | 182M |
| Pedestrian detection (lightweight) | 16.2ms | Multi-object tracking | 27M |
| Pedestrian tracking (high precision) | 31.8ms | Multi-object tracking | 182M |
| Pedestrian tracking (lightweight) | 21.0ms | Multi-object tracking | 27M |
| MTMCT(REID) | Single Person 1.5ms | REID | REID：92M |
| Attribute recognition (high precision) | Single person8.5ms | Object detection Attribute recognition |
Object detection：182M Attribute recognition：86M |
| Attribute recognition (lightweight) | Single person 7.1ms | Object detection Attribute recognition |
Object detection：182M Attribute recognition：86M |
| Falling detection | Single person 10ms | Multi-object tracking Keypoint detection Behavior detection based on key points |
Multi-object tracking：182M Keypoint detection：101M Behavior detection based on key points: 21.8M |
| Intrusion detection | 31.8ms | Multi-object tracking | 182M |
| Fighting detection | 19.7ms | Video classification | 90M |
| Smoking detection | Single person 15.1ms | Object detection Object detection based on Human Id |
Object detection：182M Object detection based on Human ID: 27M |
| Phoning detection | Single person ms | Object detection Image classification based on Human ID |
Object detection：182M Image classification based on Human ID：45M |
PP-Vehicle End-to-end model results (click to expand)
| Task | End-to-End Speed（ms） | Model | Size |
|---|---|---|---|
| Vehicle detection (high precision) | 25.7ms | object detection | 182M |
| Vehicle detection (lightweight) | 13.2ms | object detection | 27M |
| Vehicle tracking (high precision) | 40ms | multi-object tracking | 182M |
| Vehicle tracking (lightweight) | 25ms | multi-object tracking | 27M |
| Plate Recognition | 4.68ms | plate detection plate recognition |
Plate detection：3.9M Plate recognition：12M |
| Vehicle attribute | 7.31ms | attribute recognition | 7.2M |
Click to download the model, then unzip and save it in the . /output_inference.