【摘要】：由于目標(biāo)姿態(tài)、攝像頭角度、光線條件等因素的影響,行人重識(shí)別仍然是一個(gè)具有挑戰(zhàn)性的問題。目前大多數(shù)方法主要注重提高重識(shí)別精度,對(duì)實(shí)時(shí)性考慮較少。因此,本文提出了一種基于增強(qiáng)聚合通道特征(ACF)的實(shí)時(shí)行人重識(shí)別算法。利用ACF對(duì)行人進(jìn)行檢測(cè),并在此基礎(chǔ)上,結(jié)合直方圖特征和紋理特征構(gòu)成增強(qiáng)ACF,作為行人重識(shí)別的特征描述子。利用測(cè)度學(xué)習(xí)方法對(duì)重識(shí)別模型進(jìn)行訓(xùn)練。在4個(gè)數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明,與傳統(tǒng)的重識(shí)別特征相比,提出的特征描述子逼近最好的重識(shí)別準(zhǔn)確率,并且具有更快的計(jì)算速度。整個(gè)行人檢測(cè)與重識(shí)別系統(tǒng)的運(yùn)行速度達(dá)到10 frame·s~(-1)以上,基本可以滿足實(shí)時(shí)行人重識(shí)別的需求。
[Abstract]:Due to the influence of target attitude, camera angle, light condition and so on, pedestrian recognition is still a challenging problem. At present, most of the methods mainly focus on improving the recognition accuracy, but less on the real-time. Therefore, a real-time pedestrian recognition algorithm based on enhanced aggregate channel feature (ACF) is proposed in this paper. Using ACF to detect pedestrians and combining histogram features and texture features, an enhanced ACF, is used as a feature descriptor for pedestrian recognition. The method of measure learning is used to train the recognition model. The experimental results on four datasets show that the proposed feature descriptor approximates the best recognition accuracy and has a faster computing speed than the traditional re-recognition features. The speed of the whole pedestrian detection and recognition system is more than 10 frame s ~ (-1), which can meet the requirement of real-time pedestrian recognition.
【作者單位】： 中國(guó)人民解放軍空軍航空大學(xué)飛行器控制系;
【基金】：國(guó)家自然科學(xué)基金(6160011396) 吉林省教育廳“十三五”科學(xué)技術(shù)研究項(xiàng)目(吉教科合字[2016]第515號(hào))
【分類號(hào)】：TP391.41
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本文編號(hào)：2332692