本文關鍵詞： 高斯增量降維模型 流形Boltzmann優(yōu)化 人體運動形態(tài) 輪廓圖像 子向量　出處：《電子學報》2017年12期 　論文類型：期刊論文

【摘要】：為了從多視角輪廓圖像估計出含空間位置信息的三維人體運動形態(tài),該文提出高斯增量降維與流形Boltzmann優(yōu)化(GIDRMBO)算法.該算法把表示三維人體運動形態(tài)的高維數(shù)據分成表示空間位置信息和姿態(tài)信息兩段子向量后,用高斯增量降維模型(GIDRM)分別對其樣本進行降維,建立相應的低維空間及映射關系,然后在相應的低維空間使用流形Boltzmann優(yōu)化算法來對輪廓匹配目標函數(shù)進行優(yōu)化,從而實現(xiàn)估計.其中,所提算法分別利用了兩段子向量樣本的低維數(shù)據作為先驗信息,可較好的避免陷入局部最優(yōu)區(qū)域進行搜索,最終生成與各視角原始運動圖像匹配且含空間位置信息的三維人體運動形態(tài).經仿真實驗驗證,所提算法與常用粒子濾波算法相比,其估計誤差小,并且還能起到消除輪廓數(shù)據歧義和克服短時遮擋的作用.
[Abstract]:In order to estimate three-dimensional human motion form with spatial position information from multi-view contour image, In this paper, Gao Si incremental dimensionality reduction and manifold Boltzmann optimization algorithm are proposed, in which the high dimensional data representing three-dimensional human motion is divided into two subvectors: spatial position information and attitude information. Using Gao Si's incremental dimensionality reduction model (GIDRM) to reduce the dimension of the sample, the corresponding low-dimensional space and mapping relationship are established, and then the contour matching objective function is optimized by using the manifold Boltzmann optimization algorithm in the corresponding low-dimensional space. In order to realize the estimation, the proposed algorithm uses the low-dimensional data of two subvector samples as prior information, which can avoid falling into the local optimal region to search. Finally, the 3D human body motion shape, which is matched with the original motion image of each angle of view and containing the spatial position information, is generated. The simulation results show that the proposed algorithm has less estimation error than the usual particle filter algorithm. It can also play the role of eliminating the ambiguity of contour data and overcoming the short-term occlusion.
【作者單位】： 華南理工大學電子與信息學院;廣東第二師范學院計算機科學系;
【基金】：國家自然科學基金(No.61202292) 廣東省自然科學基金(No.9151064101000037) 廣東省普通高校青年創(chuàng)新人才項目(No.2016KQNCX111)
【分類號】：TP391.41

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