基于Adaboost-BP神經(jīng)網(wǎng)絡(luò)模型的姿勢(shì)識(shí)別研究

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摘要：姿勢(shì)識(shí)別是計(jì)算機(jī)視覺中的重要研究方向。本文基于BP神經(jīng)網(wǎng)絡(luò)模型構(gòu)建姿勢(shì)識(shí)別模型，并采用Adaboost迭代學(xué)習(xí)對(duì)BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)效果進(jìn)行提升。在雙手叉腰、單臂張開、跑步與散步等動(dòng)作的識(shí)別上，比CNN卷積模型具有更好的效果，并且比直接采用BP神經(jīng)網(wǎng)絡(luò)模型能夠更為精確地識(shí)別各類姿勢(shì)。

關(guān)鍵詞：機(jī)器視覺;姿勢(shì)識(shí)別;Adaboost-BP模型

doi：10.3969/J.ISSN.1672-7274.2022.06.017

中圖分類號(hào)：TM 715                 文獻(xiàn)標(biāo)示碼：A                文章編碼：1672-7274（2022）06-00-03

Research on Pose Recognition based on AdaBoost BP Neural Network Model

ZhU Rui1， XUE Wenhua 2， LI Wenai 3， WU Zhaoqi 4， QIU Xue1

（1. Chengdu Jiabaili Technology Co.， Ltd.， Chengdu 610000，China; 2. Pitong No. 1 primary school， Pidu District， Chengdu 611730，China; 3. West Branch of Chengdu Caotang primary school， Chengdu 610073，China; 4. No. 8 primary school， Karamay 834000，China）

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 AdaBoost 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.

Key words： machine vision; posture recognition; Adaboost BP model

1  研究背景

姿勢(shì)識(shí)別是計(jì)算機(jī)研究領(lǐng)域的重要問題，在現(xiàn)實(shí)中具有廣泛的應(yīng)用場(chǎng)景。（剩余3987字）