發(fā)布時(shí)間：2021-03-13 04:18

　　智能科技對(duì)現(xiàn)實(shí)環(huán)境中人類活動(dòng)的敏銳分析為研究人員提供了廣泛的應(yīng)用領(lǐng)域,如對(duì)監(jiān)控系統(tǒng)、客戶理解、購(gòu)物態(tài)度、正�；虍惓Ｐ袨榈姆治龅�。然而,由于各種各樣的局限性,如雜亂的背景、閉塞、視點(diǎn)變化等,要找到對(duì)行動(dòng)的準(zhǔn)確識(shí)別是一項(xiàng)具有挑戰(zhàn)性的任務(wù)。我們必須牢記這些在視頻中自動(dòng)識(shí)別人類行為的局限性。實(shí)時(shí)自動(dòng)識(shí)別HAR和非受控視頻信息,如“監(jiān)控視頻”便是我們的主要關(guān)注點(diǎn)。近年來(lái),研究人員試圖提高基于視頻的識(shí)別系統(tǒng)的準(zhǔn)確度和精度,但并沒(méi)有真正考慮到系統(tǒng)的效率。本研究主要考慮的是一個(gè)具有高精度值的髙效系統(tǒng)。另外,本文還重點(diǎn)研究了實(shí)時(shí)環(huán)境下的識(shí)別工具。此外,在復(fù)雜的環(huán)境中識(shí)別和分析人類行為更具有必要性與重要性。本研究的目的也在于區(qū)分正常行為與異常行為,并以系統(tǒng)的方式加以分類。綜合研究表明,最近實(shí)現(xiàn)的分類是基于復(fù)雜度以及手工提取的原始輸入特征。卷積神經(jīng)網(wǎng)絡(luò)具有直接作用于原始輸入的能力,但也有處理二維輸入的局限性。因此,本研究介紹了一種用于人體動(dòng)作識(shí)別的新型三維卷積神經(jīng)網(wǎng)絡(luò)。此外,該方法是一種全自動(dòng)的人類行為識(shí)別的深度模型。該學(xué)習(xí)過(guò)程并沒(méi)有對(duì)人類行為進(jìn)行分類的先驗(yàn)知識(shí)。因此本文建議方法包含兩個(gè)步驟:第一步,應(yīng)用三... 

【文章來(lái)源】：華中師范大學(xué)湖北省 211工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：86 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
Acknowledgements
Abstract
Chapter 1 Introduction
    1.1 General Background
    1.2 Problem Statement
    1.3 Significance of the Problem
    1.4 Contributions
    1.5 Objectives of the Research
    1.6 Thesis Outline
Chapter 2 Literature Review
    2.1 Designed Descriptor Based Methods
        2.1.1 Representation
        2.1.2 Classification
    2.2 Action Recognition Using Deep Models
        2.2.1 Convolution Neural Network
        2.2.2 Recurrent Neural Network
        2.2.3 Long Short-Term Memory Network
        2.2.4 3D-Convolution Neural Network
    2.3 Datasets for Human Action Recognition
        2.3.1 Simple Actions Datasets
        2.3.2 Complex Action Datasets
    2.4 Comparison of Our Approach with Related Work
        2.4.1 Cost and Efficiency
        2.4.2 Accuracy and Precision
Chapter 3 Proposed Method
    3.1 Representation and Classification of HAR
        3.1.1 Bag of Features Approach
        3.1.2 Fv Encoding Approach
    3.2 Theory of Convolution Neural Network
        3.2.1 Forward Propagation in Convolution Neural Network
        3.2.2 Backpropagation in Convolutional Neural Networks
        3.2.3 3D-Convolutional Neural Networks
    3.3 Proposed Method
        3.3.1 Step-1 Neural Network
        3.3.2 Step-2 Neural Network
Chapter 4 Experimental Results and Evaluation
    4.1 Feature Representation and Classification
    4.2 Brief Description of KTH and UCF11 Datasets
    4.3 Experiments on KTH and UCF11 Datasets
    4.4 Evaluation Protocol
    4.5 Results and Comparison
        4.5.1 Action Recognition on KTH dataset
        4.5.2 Action Recognition on UCF11 Dataset
    4.6 Advantages and Disadvantages of Using 3D-CNN
        4.6.1 Advantages of 3D-CNN
        4.6.2 Disadvantages of 3D-CNN
        4.6.3 Advantages of using RNN as Classifier
Chapter 5 Summary and Conclusions
    5.1 Summary
    5.2 Conclusions and Discussions
    5.3 Future Work
References
Appendix A Abstract and Summary
    A.1 Abstract
    A.2 Accepted Papers
    A.3 Environment Setting
        A.3.1 Windows Environment Setting
        A.3.2 Linux Environment Setting



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