Face Tracking Control System Based on Raspberry Pi and PID
20 May 2024
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The Basic Principle of Face Tracking
Face tracking is an important branch of computer vision, primarily involving technologies such as image processing, machine learning, and artificial intelligence. Its purpose is to detect and track the position and movement trajectory of faces in real-time video, enabling further analysis and processing of the faces. This article will introduce the basic principles of face tracking, implementation methods, and its application scenarios in real life.
The basic principles of face tracking can be divided into three steps: face detection, feature extraction, and target tracking.
- Face Detection: Face detection involves locating the position and size of faces in a video. Common algorithms include feature-based methods and deep learning-based methods. Feature-based methods detect faces using geometric features and texture information, while deep learning-based methods use trained neural networks to automatically learn and recognize faces.
- Feature Extraction: After detecting a face, it is necessary to extract facial features for subsequent recognition and tracking. Feature extraction typically includes extracting information about the facial contour, skin color, texture, and more.
- Target Tracking: Once the facial features are extracted, target tracking algorithms track the face's position and movement trajectory in the video based on these features. Common algorithms include filter-based methods and deep learning-based methods. Filter-based methods use algorithms such as Kalman filters and particle filters to track the target, while deep learning-based methods train neural networks to predict the target's movement trajectory.
Components Required to Implement this Project:
- Raspberry Pi 4B
- Two SG90 180-degree servo motors
- Two-axis servo gimbal
- Raspberry Pi CSI camera
- Breadboard
- Male-to-male jumper wires
Wiring Diagram
Tilt: The signal pin of the SG90 180-degree servo motor is connected to the PWM output pin GPIO16 on the Raspberry Pi for signal control.
Pan: The signal pin of the SG90 180-degree servo motor is connected to the PWM output pin GPIO19 on the Raspberry Pi for signal control.
Specific Steps
Download the Cascade Classifier for Face Recognition
Download the cascade classifier "haarcascade_frontalface_default.xml" from the following address: haarcascade_frontalface_default.xml. After downloading, place it in the same directory as all the subsequent files.
Experimental Phenomena
This system can be used in various application scenarios such as security monitoring, smart homes, and intelligent transportation. By recognizing and tracking faces, it can identify family members and achieve personalized environment settings. The system can implement intelligent monitoring and security functions, providing users with convenient human-machine interaction and intelligent control features.
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