無重疊視域下行人再識別算法的研究
發(fā)布時間：2018-08-20 12:18
【摘要】：近年來,隨著人們自身公共安全意識的提高以及視頻監(jiān)控技術(shù)的發(fā)展,智能視頻監(jiān)控系統(tǒng)得到了大量的普及。行人再識別(Person Re-identification)是近幾年智能視頻分析領(lǐng)域興起的一項新技術(shù),是多攝像機聯(lián)合智能視頻監(jiān)控系統(tǒng)中需要解決的關(guān)鍵問題之一,因而得到了廣大計算機視覺領(lǐng)域及人工智能領(lǐng)域相關(guān)人員的關(guān)注。無重疊視域下的行人再識別是指在一個多攝像機聯(lián)合的視頻監(jiān)控系統(tǒng)下,判斷一個攝像頭中出現(xiàn)的行人目標(biāo)是否在另一個非重疊視域監(jiān)控下的攝像頭中出現(xiàn)過的一個過程,即識別出不同攝像機拍攝到的屬于某一個行人的圖像。但由于受攝像機角度、背景變化、光照條件、姿態(tài)變化、遮擋等多種外在復(fù)雜因素的影響,同一行人在不同視域下可能存在很大的差異性,從而使得行人再識別問題具有很大的挑戰(zhàn)性。本文提出了一種基于度量學(xué)習(xí)的行人再識別算法PRML(Person Re-identification based on Metric Learning),主要通過特征學(xué)習(xí)生成一個測度矩陣來進(jìn)行行人的再識別。本文首先通過一種圖像增強算法對原始行人圖像進(jìn)行處理,從而減少因光照變化所帶來的影響,然后根據(jù)人體目標(biāo)外觀形態(tài)特性對行人進(jìn)行合理分割,并提取行人圖像顏色特征(HSV、Lab)、紋理特征(SILTP、FHOG)以及顏色屬性ColorNames特征并進(jìn)行核函數(shù)學(xué)習(xí),將原始線性特征空間投影到更加具有區(qū)分性的非線性特征空間并對特征進(jìn)行PCA降維,之后考慮到不同類型特征對行人圖像描述的差異性,分別學(xué)習(xí)得到三個獨立的測度矩陣,并通過正則化方法來優(yōu)化測度矩陣的過擬合問題,最終并加權(quán)融合多個測度矩陣綜合得到行人圖像對的相似性度量函數(shù),從而實現(xiàn)行人相似性的度量。最后在VIPeR、iLIDS、CUHK01三個公共數(shù)據(jù)集上采用CMC(Cumulative Matching Characteristic Curve)曲線評測標(biāo)準(zhǔn)對提出的算法進(jìn)行了實驗效果驗證、對比和分析。
[Abstract]:In recent years, with the improvement of public safety awareness and the development of video surveillance technology, intelligent video surveillance system has been widely used. Pedestrian rerecognition (Person Re-identification) is a new technology emerging in the field of intelligent video analysis in recent years. It is one of the key problems to be solved in multi-camera joint intelligent video surveillance system. As a result, the field of computer vision and artificial intelligence related to the field of attention. Pedestrian reidentification without overlap is a process in which a video surveillance system with multiple cameras is used to determine whether the pedestrian target in one camera has appeared in another camera. That is to identify the different cameras of the image of a pedestrian. However, due to the influence of many complicated external factors, such as camera angle, background change, illumination condition, attitude change, occlusion and so on, the same line of people may have great differences in different visual fields. Therefore, the problem of pedestrian recognition is very challenging. In this paper, a new pedestrian rerecognition algorithm based on metric learning (PRML (Person Re-identification based on Metric Learning),) is proposed, which is based on feature learning to generate a measure matrix for pedestrian rerecognition. In this paper, the original pedestrian image is processed by an image enhancement algorithm, so as to reduce the influence caused by the change of illumination, and then the pedestrian is segmented reasonably according to the appearance and morphological characteristics of the human object. The color feature (HSV), texture feature (SILTPFHOG) and color attribute (ColorNames) of pedestrian image are extracted, and the kernel function is studied. The original linear feature space is projected into a more discriminative nonlinear feature space and the feature dimension is reduced by PCA. Considering the difference of pedestrian image description between different types of features, three independent measure matrices are obtained, and the overfitting problem of measure matrix is optimized by regularization method. Finally, the similarity measurement function of pedestrian image pairs is obtained by combining multiple measure matrices weighted and weighted, and the pedestrian similarity measurement is realized. Finally, the experimental results of the proposed algorithm are verified, compared and analyzed by using CMC (Cumulative Matching Characteristic Curve) curve evaluation standard on three common data sets of VIPeR iLIDSU CUHK01.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TN948.6
本文編號：2193585
[Abstract]:In recent years, with the improvement of public safety awareness and the development of video surveillance technology, intelligent video surveillance system has been widely used. Pedestrian rerecognition (Person Re-identification) is a new technology emerging in the field of intelligent video analysis in recent years. It is one of the key problems to be solved in multi-camera joint intelligent video surveillance system. As a result, the field of computer vision and artificial intelligence related to the field of attention. Pedestrian reidentification without overlap is a process in which a video surveillance system with multiple cameras is used to determine whether the pedestrian target in one camera has appeared in another camera. That is to identify the different cameras of the image of a pedestrian. However, due to the influence of many complicated external factors, such as camera angle, background change, illumination condition, attitude change, occlusion and so on, the same line of people may have great differences in different visual fields. Therefore, the problem of pedestrian recognition is very challenging. In this paper, a new pedestrian rerecognition algorithm based on metric learning (PRML (Person Re-identification based on Metric Learning),) is proposed, which is based on feature learning to generate a measure matrix for pedestrian rerecognition. In this paper, the original pedestrian image is processed by an image enhancement algorithm, so as to reduce the influence caused by the change of illumination, and then the pedestrian is segmented reasonably according to the appearance and morphological characteristics of the human object. The color feature (HSV), texture feature (SILTPFHOG) and color attribute (ColorNames) of pedestrian image are extracted, and the kernel function is studied. The original linear feature space is projected into a more discriminative nonlinear feature space and the feature dimension is reduced by PCA. Considering the difference of pedestrian image description between different types of features, three independent measure matrices are obtained, and the overfitting problem of measure matrix is optimized by regularization method. Finally, the similarity measurement function of pedestrian image pairs is obtained by combining multiple measure matrices weighted and weighted, and the pedestrian similarity measurement is realized. Finally, the experimental results of the proposed algorithm are verified, compared and analyzed by using CMC (Cumulative Matching Characteristic Curve) curve evaluation standard on three common data sets of VIPeR iLIDSU CUHK01.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TN948.6
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