基于雙目視覺的人體行為分析技術(shù)研究
[Abstract]:Human behavior analysis is a hot topic in the field of computer vision. This technology has broad application prospects in many fields such as video surveillance, perceptual interface, motion analysis and virtual reality. How to effectively overcome the influence of occlusion and polysemy, the complexity of environment and the non-rigid nature of human body has become an important task in human behavior analysis technology. Based on this, this paper focuses on the research of human behavior analysis technology based on binocular vision. The methods of stereo matching and depth information acquisition based on binocular vision and the algorithm of human behavior analysis based on convolutional neural network are analyzed and studied, and some solutions and improvement measures are put forward. The main contents of this paper are as follows: 1. In the research of stereo matching and depth information acquisition algorithm based on binocular vision, A stereo matching algorithm combining SURF (Speeded-Up Robust Features- SURF) and region matching based on human edge information is proposed. The algorithm aims to reduce the influence of occlusion and polysemy and improve the accuracy of behavior analysis algorithm by introducing 3D depth information. The method includes four parts: binocular vision system calibration, moving target detection, SURF stereo matching and region matching optimization, and 3D information acquisition. After the calibration of the binocular vision system was completed by using the plane template two-step method, the background difference method of the improved mixed Gao Si model was used to extract the moving target of human body. In the process of matching, the human body edge information is first matched by SURF, and then the matching result is optimized by combining the region matching algorithm based on limit constraint to improve the accuracy of human body feature point matching. Finally, the 3D depth information is obtained according to the matching points. The experimental results show that the algorithm can accurately obtain the three-dimensional coordinates of human body and avoid the interference of occlusion and polysemy. 2. In the research of human behavior analysis algorithm based on binocular vision, A human behavior analysis algorithm based on small sample convolution neural network (Convolutional Neural Networks- for short CNN) is proposed. Convolution neural network is divided into feature extraction layer and feature mapping layer. In the feature extraction layer, the CNN neuron is used to perceive and extract the local features, and then the network layer composed of multiple feature mapping layers is used to calculate the feature extraction accuracy more accurately and reliably. The human behavior analysis algorithm based on small sample convolution neural network uses CNN method to classify and recognize the images collected by left and right cameras in binocular vision system, and then carries on the weight fusion processing to the recognition results of left and right images. By adjusting the system parameters, a higher behavior matching degree can be obtained. The experimental results show that the algorithm can accurately identify single action and interactive action, and improve the recognition rate of human body behavior analysis algorithm.
【學(xué)位授予單位】:北方工業(yè)大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類號(hào)】:TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 李錦明;閆曉俊;江旭東;溫杰;郇_";;Sobel圖像邊沿檢測算法的優(yōu)化設(shè)計(jì)與實(shí)現(xiàn)[J];電子技術(shù)應(yīng)用;2016年03期
2 劉洪彬;常發(fā)亮;;權(quán)重系數(shù)自適應(yīng)光流法運(yùn)動(dòng)目標(biāo)檢測[J];光學(xué)精密工程;2016年02期
3 楊景豪;劉巍;劉陽;王福吉;賈振元;;雙目立體視覺測量系統(tǒng)的標(biāo)定[J];光學(xué)精密工程;2016年02期
4 趙奇可;孫延奎;;快速定位圖像尺度和區(qū)域的3維跟蹤算法[J];中國圖象圖形學(xué)報(bào);2016年01期
5 李寰宇;畢篤彥;查宇飛;楊源;;一種易于初始化的類卷積神經(jīng)網(wǎng)絡(luò)視覺跟蹤算法[J];電子與信息學(xué)報(bào);2016年01期
6 趙燕偉;任設(shè)東;陳尉剛;樓炯炯;冷龍龍;;基于改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的可拓分類器構(gòu)建[J];計(jì)算機(jī)集成制造系統(tǒng);2015年10期
7 楊宇翔;高明煜;尹克;吳占雄;;結(jié)合同場景立體圖對(duì)的高質(zhì)量深度圖像重建[J];中國圖象圖形學(xué)報(bào);2015年01期
8 熊英;;基于背景和幀間差分法的運(yùn)動(dòng)目標(biāo)提取[J];計(jì)算機(jī)時(shí)代;2014年03期
9 鐘靈;章云;;等級(jí)閾值的彩色圖像矢量中值濾波[J];中國圖象圖形學(xué)報(bào);2011年03期
10 顏軻;萬國偉;李思昆;;基于圖像分割的立體匹配算法[J];計(jì)算機(jī)應(yīng)用;2011年01期
相關(guān)碩士學(xué)位論文 前5條
1 王培培;基于視頻的人體動(dòng)作識(shí)別研究[D];南京郵電大學(xué);2013年
2 朱巖;復(fù)雜場景中的時(shí)空特征學(xué)習(xí)與人體行為分析[D];上海交通大學(xué);2012年
3 王艷;基于點(diǎn)特征的立體匹配算法研究[D];南京理工大學(xué);2009年
4 吳亞鵬;基于雙目視覺的運(yùn)動(dòng)目標(biāo)跟蹤與三維測量[D];西北大學(xué);2008年
5 宰小濤;基于SIFT特征描述子的立體匹配算法研究[D];上海交通大學(xué);2007年
,本文編號(hào):2386312
本文鏈接:http://www.wukwdryxk.cn/shoufeilunwen/xixikjs/2386312.html