本文選題：行人重識別 + 超像素　； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：在視頻監(jiān)控領(lǐng)域,視頻監(jiān)看人員需要跨越多個(gè)攝像頭對人群進(jìn)行分析,從而識別出某個(gè)特定的已經(jīng)出現(xiàn)過的人。研究人員將這種在多個(gè)監(jiān)控視頻環(huán)境下行人目標(biāo)檢索問題稱為行人重識別問題。由于監(jiān)控視頻中的行人受視角變化、姿態(tài)變化、光照變化等因素影響,常常導(dǎo)致行人在不同攝像頭下外貌發(fā)生較為明顯的變化。圍繞這些問題,本文研究了一種基于顯著特征的行人重識別方法。在特征提取階段,本文研究了一種基于超像素的特征表示方法。該方法首先將行人圖像分割成若干超像素塊,在此基礎(chǔ)上結(jié)合顏色直方圖和加速魯棒特征構(gòu)造特征空間。實(shí)驗(yàn)證明通過將該特征描述子與現(xiàn)有的塊匹配方法相結(jié)合,極大地提高了行人重識別問題的運(yùn)行效率與算法精度。傳統(tǒng)的基于顯著特征重識別方法通過不同樣本的差異表征自身權(quán)重。然而這種計(jì)算結(jié)果不夠穩(wěn)定,它可能隨著比較樣本的變化而變化。因此本文介紹了一種基于元胞自動(dòng)機(jī)的方法計(jì)算行人圖像的內(nèi)在顯著特征。為了充分利用上述兩種方法的優(yōu)點(diǎn),本文利用多層元胞自動(dòng)機(jī)將它們?nèi)诤蠌亩趯?shí)驗(yàn)中獲得了更好的效果。最后在相似性度量過程中僅僅根據(jù)各子塊的顯著與否決定匹配權(quán)重過于簡單,本文在此基礎(chǔ)上研究了一種學(xué)習(xí)排序方法衡量各圖像之間的相似性。實(shí)驗(yàn)結(jié)果表明,與現(xiàn)有的算法相比在i LIDS數(shù)據(jù)庫上本文算法表現(xiàn)出了更好的性能。
[Abstract]:In the field of video surveillance, video monitors need to analyze people across multiple cameras to identify a particular person who has already appeared. The problem of pedestrian target retrieval in multiple surveillance video environments is called pedestrian recognition problem by researchers. Due to the change of visual angle, posture, illumination and other factors in the video, the appearance of the pedestrian changes obviously under different cameras. To solve these problems, a pedestrian recognition method based on salient features is studied in this paper. In the phase of feature extraction, a feature representation method based on hyperpixel is studied in this paper. Firstly, the pedestrian image is divided into several super-pixel blocks, and then the color histogram and the accelerated robust feature are combined to construct the feature space. The experimental results show that by combining the feature descriptor with the existing block matching methods, the running efficiency and the algorithm accuracy of the pedestrian recognition problem are greatly improved. Traditional recognition methods based on salient features represent their weight by different samples. However, the results of this calculation are not stable and may vary with the variation of the comparison samples. Therefore, this paper introduces a cellular automaton based method for calculating the inherent salient features of pedestrian images. In order to make full use of the advantages of the above two methods, this paper uses multilayer cellular automata to fuse them and obtain better results in experiments. Finally, in the process of similarity measurement, it is too simple to determine the matching weight based on the salience or not of each sub-block. On the basis of this, we study a learning sorting method to measure the similarity between images. Experimental results show that the proposed algorithm performs better on I LIDS database than the existing algorithms.
【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

【參考文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 王亦民;面向監(jiān)控視頻的行人重識別技術(shù)研究[D];武漢大學(xué);2014年

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本文編號：1819799