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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.
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🚶♂️🚶♀️ 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).
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🚗🚙 PP-Vehicle has four major toolbox for vehicle analysis: The license plate recognition、vechile attributes、in-out counting、illegal_parking recognition.
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🔥🔥🔥 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.
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🔥 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.
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2022.4.18: Add PP-Human practical tutorials, including training, deployment, and action expansion. Details for AIStudio project please see Link
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2022.4.10: Add PP-Human examples; empower refined management of intelligent community management. A quick start for AIStudio Link
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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.
PP-Human End-to-end model results (click to expand)
Task | End-to-End Speed(ms) | Model | Size |
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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 |
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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
.