基于雙目視覺的人體行為分析技術研究
發(fā)布時間：2018-12-18 18:34
【摘要】：人體行為分析技術是計算機視覺領域的一個研究熱點問題。該技術在視頻監(jiān)控、感知接口、運動分析和虛擬現實等多個領域均具有廣闊的應用前景。其中如何有效克服遮擋和多義性、環(huán)境的復雜變化性以及人體的非剛體性等困難的影響成為人體行為分析技術中的一個重要任務�；诖�,本文圍繞基于雙目視覺的人體行為分析技術展開研究,重點針對基于雙目視覺的立體匹配與深度信息獲取方法和基于卷積神經網絡的人體行為分析算法展開了分析與研究,提出了一些解決方法和改進措施。本文研究的主要內容如下:1、在基于雙目視覺的立體匹配與深度信息獲取算法研究中,提出了一種基于人體邊緣信息的SURF(Speeded-Up Robust Features-簡稱SURF)與區(qū)域匹配結合的立體匹配算法。該算法旨在降低遮擋和多義性造成的影響,引入三維深度信息提高行為分析算法的精度。該方法包括雙目視覺系統(tǒng)標定、運動目標檢測、SURF立體匹配與區(qū)域匹配優(yōu)化、三維信息獲取四個部分。在采用平面模板兩步法完成雙目視覺系統(tǒng)的標定后,采用改進的混合高斯模型的背景差分法提取人體運動目標。在匹配過程中,先對獲取的人體邊緣信息進行SURF匹配,然后結合基于極限約束的區(qū)域匹配算法進一步優(yōu)化匹配結果,提高人體特征點匹配的精度。最后根據得到的匹配點獲取三維深度信息。實驗結果表明,該算法能夠準確獲取人體三維空間坐標,有效避免遮擋和多義性的干擾。2、在基于雙目視覺的人體行為分析算法研究中,提出了一種基于小樣本卷積神經網絡(Convolutional Neural Networks-簡稱CNN)的人體行為分析算法。卷積神經網絡分為特征提取層和特征映射層。在特征提取層,利用CNN神經元感知并提取局部特征;然后利用由多個特征映射層組成的網絡層進行相應的計算,使得特征提取精度更為準確可靠�；谛颖揪矸e神經網絡的人體行為分析算法分別對雙目視覺系統(tǒng)下左右相機采集的圖像采用CNN方法進行分類識別,然后對左右圖像的識別結果進行權值融合處理,通過調節(jié)系統(tǒng)參數,獲取更高的行為匹配度。實驗結果表明,該算法能夠對單人動作和交互動作進行準確識別,有效提高人體行為分析算法的識別率。
[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.
【學位授予單位】：北方工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2386312
[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.
【學位授予單位】：北方工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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