Maoyuan Luo
ABSTRACT
This project proposes an intelligent aquatic lifesaving robot based on computer vision and machine learning technology. The robot realizes high-precision positioning through multi-satellite positioning function, and is equipped with posture sensing function and computer vision function. It uses Convolutional neural network and Convolutional Pose Machine algorithm to realize human posture recognition. At the same time, the robot has the function of self-balance adjustment and wireless charging, which improves the stability and sustainability of the robot. The experimental results show that the effect of action recognition using 3D convolutional neural network is better than the traditional k-nearest neighbor classifier and support vector machine classifier. In the Weizmann action database, the recognition rate of 3D convolutional neural network reaches 96.3%, which is significantly higher than 90.6% of K-nearest classifier and 93.1% of support vector machine classifier. In the KTH action database, the recognition rate of 3D convolutional neural network is 90.3%, which is higher than 86.7% of K-nearest classifier and 89% of support vector machine classifier.
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