本文選題：步態(tài)識(shí)別 + 步態(tài)幀差熵圖　； 參考：《西安科技大學(xué)》2017年碩士論文

【摘要】：隨著智能化程度的逐步深入,公共場(chǎng)合的安全形勢(shì)日益嚴(yán)峻,如何確保公共安全成為人們關(guān)心和矚目的焦點(diǎn),而有效的身份識(shí)別技術(shù)則是確保公共安全的關(guān)鍵。生物特征以其安全、穩(wěn)定、可靠等特點(diǎn)廣泛應(yīng)用于智能監(jiān)控領(lǐng)域,較之于人臉、指紋等,步態(tài)以其非侵犯、遠(yuǎn)距離、難以隱藏等優(yōu)勢(shì)受到了大批研究者的關(guān)注。近年來(lái),多視角下的步態(tài)識(shí)別問(wèn)題一直是步態(tài)識(shí)別研究的一個(gè)熱點(diǎn),因此本文在多視角的背景下,重點(diǎn)研究了步態(tài)特征的提取以及視角歸一化問(wèn)題。本文完成的主要研究工作如下:首先,采用背景差分法完成目標(biāo)的提取。針對(duì)目標(biāo)圖像中含有噪聲及不連通等問(wèn)題,對(duì)其采用形態(tài)學(xué)方法進(jìn)行去噪,并進(jìn)行連通區(qū)域分析來(lái)獲得較完整的二值化步態(tài)圖像。由于步態(tài)是一個(gè)周期性的運(yùn)動(dòng),一個(gè)周期內(nèi)的步態(tài)變化更能反映人體的運(yùn)動(dòng)特性,因此本文根據(jù)步態(tài)輪廓寬度變化來(lái)檢測(cè)步態(tài)周期,并對(duì)步態(tài)圖像進(jìn)行標(biāo)準(zhǔn)化處理。其次,人體的步態(tài)圖像序列不僅含有靜態(tài)的步態(tài)信息,同時(shí)相鄰步態(tài)間的變化也隱含了豐富的動(dòng)態(tài)信息,而常用的步態(tài)能量圖和步態(tài)幀差能量圖只考慮了其中的靜態(tài)信息和部分動(dòng)態(tài)信息,因此本文將刻畫(huà)不確定性的熵引入到步態(tài)幀差能量圖中,提出采用步態(tài)幀差熵圖刻畫(huà)步態(tài)特征,再采用最近鄰分類(lèi)法完成分類(lèi)識(shí)別。最后,針對(duì)多視角步態(tài)識(shí)別過(guò)程復(fù)雜、計(jì)算量大等問(wèn)題,本文提出基于低秩優(yōu)化的視角歸一化方法進(jìn)行步態(tài)識(shí)別。在步態(tài)特征圖像的基礎(chǔ)上,將任意視角下的步態(tài)特征圖像采用秩優(yōu)化的方法歸一化到秩最小的視角,再采用最近鄰分類(lèi)法進(jìn)行跨視角下的識(shí)別驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,本文提出的步態(tài)幀差熵圖的識(shí)別率要高于步態(tài)能量圖和步態(tài)幀差能量圖,而基于步態(tài)幀差熵圖的視角歸一化步態(tài)識(shí)別方法也在一定程度上提高了跨視角下的識(shí)別率。
[Abstract]:With the gradual deepening of intelligence, the security situation in public places is becoming increasingly serious. How to ensure public safety has become the focus of concern and attention, and effective identification technology is the key to ensure public safety. Biological features are widely used in the field of intelligent surveillance because of their security, stability and reliability. Compared with face, fingerprint and so on, gait has attracted a lot of researchers' attention because of its advantages of non-invasive, long-distance and difficult to hide. In recent years, gait recognition under multiple angles has been a hot topic in gait recognition. Therefore, this paper focuses on gait feature extraction and angle normalization under the background of multi-angle. The main work of this paper is as follows: firstly, the background difference method is used to extract the target. Aiming at the problems of noise and disconnection in the target image, the morphological method is used to de-noise and the connected region is analyzed to obtain a complete binary gait image. Gait is a periodic movement, and the gait changes in a period can reflect the movement characteristics of human body. Therefore, the gait period is detected according to the gait contour width change, and the gait image is standardized. Secondly, the gait image sequence of human body contains not only static gait information, but also rich dynamic information in the change of adjacent gait. However, the gait energy map and gait frame difference energy graph only consider the static information and some dynamic information, so the entropy which depicts uncertainty is introduced into the gait frame difference energy graph. Gait frame difference entropy graph is used to describe gait features, and the nearest neighbor classification method is used to complete classification and recognition. Finally, aiming at the complex process of multi-view gait recognition and the large amount of computation, this paper proposes a new method of gait recognition based on low-rank optimization. On the basis of gait feature image, the gait feature image with arbitrary angle of view is normalized to the angle of view with minimum rank by rank optimization method, and the nearest neighbor classification method is used to verify the recognition of gait feature image under cross-angle. The experimental results show that the recognition rate of the proposed gait difference entropy map is higher than that of gait energy map and gait frame difference energy map. The normalized gait recognition method based on gait frame difference entropy also improves the recognition rate of cross-angle gait to some extent.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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