智能交通系統(tǒng)Intelligent Transportation System（ITS）是保證城市車輛安全、平穩(wěn)運行的關(guān)鍵系統(tǒng)之一。車輛再識別Re-Identification（Re-Id）是一項重要的工作,其定義為識別不同監(jiān)控攝像頭拍攝的不重疊視野圖像中的車輛。換言之,一個攝像頭中捕捉到的某個車輛是否出現(xiàn)在多個攝像網(wǎng)絡(luò)中。隨著對自動化視頻分析需求的不斷增加,車輛再識別正受到越來越多的關(guān)注。它能支持許多關(guān)鍵應用,如智能停車、可疑車輛跟蹤、車輛事件檢測、跨攝像頭識別、道路通行限制管理系統(tǒng)和自動收費等。在過去的幾年中,各種強大的計算機視覺方法被用來分析車輛再識別任務(wù)中監(jiān)控攝像機的視頻。然而,由于需求的特殊性,研究人員在設(shè)計魯棒高效的模型來解決相關(guān)問題時面臨著很大的挑戰(zhàn),比如類間相似度、視點變化、部分遮擋、類內(nèi)可變性、背景雜波和跨數(shù)據(jù)集的車輛再識別等問題;當前提出的模型并不能十分有效地處理上述問題。本文旨在探索解決該問題的不同方法,基于深度學習的技術(shù)以獲得更好的車輛再識別性能。首先,我們改進了一個快速的交通監(jiān)控模型來識別攝像網(wǎng)絡(luò)中發(fā)現(xiàn)的不同類型的車輛。采用深卷積神經(jīng)網(wǎng)絡(luò)Convolution... 

【文章頁數(shù)】：132 頁

【學位級別】：博士

【文章目錄】：
摘要
ABSTRACT
Chapter1 Introduction
    1.1 Research Background and Significance
    1.2 Problem Statement
    1.3 Motivation
    1.4 Research Aims and Objectives
    1.5 Contributions of Dissertation
    1.6 Outline of the Dissertation
Chapter2 Literature Review
    2.1 Intelligent Transportation System(ITS)
        2.1.1 Video Surveillance
    2.2 Detection
    2.3 Recognition
    2.4 Identification
    2.5 Re-Identification
        2.5.1 Person Re-Identification
        2.5.2 Vehicle Re-Identification
        2.5.3 Methods used for Vehicle Re-Identification
        2.5.4 Performance Comparison of Vision-based State-of-the-Art Vehicle Re-Identification Approaches
        2.5.5 Potential Problems and Challenges in Vision-based Vehicle Re-Identification
        2.5.6 Publically Available Vehicle Re-Identification Datasets
        2.5.7 Evaluation Measures for Vehicle Re-Identification System
        2.5.8 Practical Applications of Vehicle Re-Identification System
    2.6 Deep Learning Basics
        2.6.1 Convolutional Neural Network
        2.6.2 Siamese Neural Network
        2.6.3 Transfer Learning
    2.7 Summary
Chapter3 Fast and Deep CNN-Model for Vehicle Type Identification
    3.1 Introduction
    3.2 Proposed Scheme and Methodology
        3.2.1 Transfer Learning using Inception-v3 Model
        3.2.2 Support Vector Machine
        3.2.3 k-Nearest Neighbors
        3.2.4 Convolutional Neural Network(CNN)Model
    3.3 Experimental Procedure
    3.4 Implementation Details
    3.5 Datasets
        3.5.1VeRi-776
        3.5.2 Vehicle ID
        3.5.3 Vehicle Re Id
        3.5.4 MIO-TCD
    3.6 Evaluation Metrics
    3.7 Experimental Results
    3.8 Applications of Traffic Surveillance System
    3.9 Summary
Chapter4 Efficient and Deep Vehicle Re-Id using Multi-Level Feature Extraction
    4.1 Introduction
    4.2 Overview of the Proposed Method
        4.2.1 Appearance Attributes-based Vehicle Re-Identification
        4.2.2 License Plate-based Vehicle Re-Identification
    4.3 Experiment and Analysis
        4.3.1 Dataset
        4.3.2 Implementation Details
    4.4 Experimental Evaluations
        4.4.1 Evaluation of Appearance-based Vehicle Filtering
        4.4.2 Evaluation of License plate-based Vehicle Re-Identification
        4.4.3 Performance Comparison with State-of-the-Art Methods
        4.4.4.Ablation Studies
    4.5 Summary
Chapter5 Visual Features with Spatio-temporal Based Fusion Model for Cross-dataset Fast Vehicle Re-Id
    5.1 Introduction
    5.2 Overview of Proposed Approach
        5.2.1 Data Augmentation
        5.2.2 Siamese Neural Network-based Classifier
        5.2.3 Spatio-temporal Pattern
        5.2.4 Calculation of Composite Similarity Score
    5.3 Experiment and Analysis
        5.3.1 Vehicle Re-Identification Benchmark Datasets
        5.3.2 Implementation Details
        5.3.3 Evaluation Measures
        5.3.4 Vehicle Re-Identification Dataset Classification
        5.3.5 Single Dataset Vehicle Re-Identification
        5.3.6 Cross Dataset Vehicle Re-Identification
    5.4 Discussion
    5.5 Summary
Chapter6 Conclusion and Future Work
    6.1 Concluding Remarks
    6.2 Future Work
Acknowledgement
References
Research Results Achieved During the Study for Doctoral Degree



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