MATLAB Implementation of Motion Detection Methods with Code Examples

Motion detection methods can be implemented through various approaches, with the most basic technique involving tracking trajectories of human body joints for detection. This primitive implementation typically uses frame differencing or background subtraction algorithms in MATLAB, where functions like `vision.ForegroundDetector` and `opticalFlow` can be employed for initial motion segmentation. For different application scenarios, various improvements can be incorporated to enhance detection accuracy and efficiency. For instance, implementing deep learning models using MATLAB's Deep Learning Toolbox with architectures like CNN or LSTM can learn complex motion patterns more effectively. The code implementation might involve using `trainNetwork` function with pre-trained models or custom architectures for feature extraction. Alternatively, integrating sensor data through MATLAB's Data Acquisition Toolbox enables multimodal motion detection, where fusion algorithms like Kalman filters can combine visual and inertial measurement unit (IMU) data. This approach typically requires implementing sensor data synchronization and multimodal feature fusion functions. In summary, depending on specific application requirements, different enhancement strategies can be implemented to achieve more efficient and accurate motion detection methods through appropriate algorithm selection and MATLAB toolbox utilization.