本文選題：動(dòng)態(tài)手勢(shì)識(shí)別 + 手勢(shì)主方向��； 參考：《電子學(xué)報(bào)》2017年09期

【摘要】：針對(duì)目前動(dòng)態(tài)手勢(shì)識(shí)別方法受手勢(shì)旋轉(zhuǎn)、平移、縮放的影響,并解決手勢(shì)識(shí)別的實(shí)時(shí)性問題,提出一種基于手勢(shì)二進(jìn)制編碼和類-Hausdorff距離模板匹配的手勢(shì)識(shí)別方法.首先,把分割好的手勢(shì)圖像進(jìn)行標(biāo)準(zhǔn)化處理,求出標(biāo)準(zhǔn)化圖像中的手勢(shì)主方向,建立二維手勢(shì)直角坐標(biāo)系,提取空間手勢(shì)特征;其次,根據(jù)前五幀手勢(shì)圖像中手勢(shì)像素點(diǎn)個(gè)數(shù)的變化量識(shí)別出動(dòng)態(tài)手勢(shì)類型;然后,用手勢(shì)二進(jìn)制描述子從動(dòng)態(tài)手勢(shì)類型中再篩選出可能的候選手勢(shì)集合;最后,用類-Hausdorff距離模板匹配方法從候選手勢(shì)集合中識(shí)別出最終手勢(shì).主要?jiǎng)?chuàng)新點(diǎn)在于:提出的動(dòng)態(tài)手勢(shì)類型識(shí)別和手勢(shì)二進(jìn)制描述子匹配的方法,大大縮短了動(dòng)態(tài)手勢(shì)識(shí)別的時(shí)間;提出的結(jié)合手勢(shì)主方向的類-Hausdorff距離方法,不僅對(duì)旋轉(zhuǎn)、平移和縮放手勢(shì)具有不變性,而且對(duì)區(qū)分度較小的手勢(shì)也具有較高的識(shí)別準(zhǔn)確率.實(shí)驗(yàn)結(jié)果表明,在光照相對(duì)穩(wěn)定的條件下,該方法能夠?qū)崟r(shí)準(zhǔn)確的實(shí)現(xiàn)動(dòng)態(tài)手勢(shì)識(shí)別,總體識(shí)別率達(dá)到95%以上,對(duì)發(fā)生縮放的手勢(shì)識(shí)別率能達(dá)到92%以上,對(duì)發(fā)生旋轉(zhuǎn)的手勢(shì)識(shí)別率能達(dá)到87%以上.本文算法已經(jīng)在一個(gè)基于手勢(shì)的人機(jī)交互界面中得到應(yīng)用.
[Abstract]:Aiming at the influence of rotation, translation and scaling of gesture, and solving the real-time problem of gesture recognition, a gesture recognition method based on gesture binary coding and matching of -Hausdorff distance template is proposed. First of all, the segmented gesture image is standardized, the main direction of the gesture in the standardized image is obtained, the 2D gesture coordinate system is established, and the spatial gesture features are extracted. The dynamic gesture types are identified according to the number of gesture pixels in the first five frames of gesture images. Then, the possible candidate gesture sets are selected from the dynamic gesture types using gesture binary descriptors. The final gesture is identified from the candidate gesture set by using the -Hausdorff distance template matching method. The main innovation lies in: the proposed method of dynamic gesture type recognition and gesture binary description sub-matching greatly shortens the time of dynamic gesture recognition, and the proposed method combines the main direction of gesture with the class -Hausdorff distance method, which is not only for rotation, but also for dynamic gesture recognition. The translation and scaling gestures are invariant, and the recognition accuracy is higher for the less discriminant gestures. The experimental results show that under the condition of relatively stable illumination, the method can realize dynamic gesture recognition in real time and accurately, the overall recognition rate is more than 95%, and the recognition rate for zoom gesture can reach more than 92%. The recognition rate of the rotation gesture can reach more than 87%. This algorithm has been applied in a gesture-based human-computer interface.
【作者單位】： 濟(jì)南大學(xué)信息科學(xué)與工程學(xué)院;山東省網(wǎng)絡(luò)環(huán)境智能計(jì)算技術(shù)重點(diǎn)實(shí)驗(yàn)室;山東省分布式計(jì)算機(jī)軟件新技術(shù)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(No.61472163,No.61603151) 國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(No.2016YFB1001403) 山東省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(No.2015GGX101025) 濟(jì)南大學(xué)博士科研基金(No.XBS1534)
【分類號(hào)】：TP391.41

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本文編號(hào)：1863451