發(fā)布時間：2018-05-21 20:07

  本文選題：行人再識別 + 度量學習��； 參考：《電子科技大學》2017年碩士論文

【摘要】：隨著計算機視覺和模式識別的發(fā)展,行人再識別已成為防止?jié)撛诒┝κ录�發(fā)生的有力工具。行人再識別是在非重疊視域中匹配同一行人目標的過程。由于從不同視角采集的行人圖像分辨率低,存在光照、姿態(tài)、背景變化及遮擋等問題,所以行人再識別一直是一項挑戰(zhàn)性的課題。為了克服這些問題,行人再識別技術分別從兩個不同的方面著手:提取魯棒性的行人特征和學習合適的距離度量。在本論文中,我更多地關注后者。針對行人再識別問題,本論文的主要工作如下:1.對行人再識別技術進行概述。首先對行人再識別技術的背景、意義和發(fā)展歷史進行了簡單闡述。然后根據行人再識別技術的側重點不同分別從特征提取和度量學習兩個方面闡述了現有的行人再識別方法。2.研究基于核度量學習的行人再識別方法。核方法最大的優(yōu)勢就是在不知道具體的非線性映射函數的形式下,就可以將原始空間的數據向高維空間投影來提高分類能力。本文基于大間隔最近鄰(LMNN)、局部費舍判別分析(LFDA)和Null Foley Sammon變換(NFST)提出了三種核度量學習方法。在三個具有挑戰(zhàn)性的行人再識別數據庫上的實驗結果驗證了核度量學習方法的有效性。3.提出基于非對稱幾何度量學習的行人再識別方法。它從幾何關系的角度針對每個特定的視角學習投影變換來提高對稱度量學習方法在行人再識別上的性能。對稱的度量學習方法對所有的視角學習單一的投影變換,然而這往往忽略了不同視角之間的差異性�；�于非對稱幾何度量學習的方法可以解決對稱度量學習在行人再識別上的上述問題。在三個挑戰(zhàn)性的行人再識別數據庫上的識別性能,證明了其在行人再識別問題上的有效性。
[Abstract]:With the development of computer vision and pattern recognition, pedestrian recognition has become a powerful tool to prevent potential violence. Pedestrian recognition is a process of matching the same pedestrian target in a non-overlapping horizon. Due to the low resolution of pedestrian images collected from different angles of view, there are problems such as illumination, posture, background change and occlusion, so pedestrian recognition is always a challenging task. In order to overcome these problems, the pedestrian recognition technique starts from two different aspects: extracting robust pedestrian features and learning appropriate distance metrics. In this paper, I pay more attention to the latter. The main work of this thesis is as follows: 1. The technology of pedestrian recognition is summarized. Firstly, the background, significance and development history of pedestrian recognition technology are briefly described. Then, according to the different emphases of pedestrian rerecognition technology, this paper expounds the existing pedestrian rerecognition methods from two aspects: feature extraction and measurement learning. A method of pedestrian rerecognition based on kernel metric learning is studied. The biggest advantage of the kernel method is that it can project the data of the original space to the high-dimensional space without knowing the specific nonlinear mapping function to improve the classification ability. In this paper, three kernel metric learning methods are proposed based on large interval nearest neighbor LMNNs, local Fisher discriminant analysis (LFDA) and Null Foley Sammon transform. Experimental results on three challenging pedestrian recognition databases verify the effectiveness of the kernel metric learning method. A method of pedestrian rerecognition based on asymmetric geometric metric learning is proposed. In order to improve the performance of the symmetric metric learning method in pedestrian recognition, the projection transformation is studied from the angle of geometric relation for each particular angle of view. Symmetric metric learning methods learn a single projection transformation for all visual angles, but this often ignores the differences between different perspectives. The method based on asymmetric geometric metric learning can solve the above problem of pedestrian rerecognition in symmetric metric learning. The recognition performance on three challenging pedestrian rerecognition databases proves its effectiveness in pedestrian rerecognition.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP181

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