基于Adaboost-BP神经网络模型的姿势识别研究

Research on Pose Recognition based on Ada Boost BP Neural Network Model

姿势识别是计算机视觉中的重要研究方向。本文基于BP神经网络模型构建姿势识别模型,并采用Adaboost迭代学习对BP神经网络模型的预测效果进行提升。在双手叉腰、单臂张开、跑步与散步等动作的识别上,比CNN卷积模型具有更好的效果,并且比直接采用BP神经网络模型能够更为精确地识别各类姿势。

Absrtact:Pose recognition is an important research direction in computer vision.This paper constructs a pose recognition model based on BP neural network model,and uses Ada Boost iterative learning to improve the prediction effect of BP neural network model.It has better effect than CNN convolution model in the recognition of movements such as hands on hips,one arm opening,running and walking,and can recognize all kinds of postures more accurately than directly using BP neural network model.