本文關(guān)鍵詞： 智能交通 行人檢測 特征融合 卷積神經(jīng)網(wǎng)絡(luò)　出處：《哈爾濱理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科技水平的不斷發(fā)展,智能交通技術(shù)愈加完善,其應(yīng)用場景及需求量不斷擴大。相較于傳統(tǒng)的智能交通系統(tǒng),新一代智能交通系統(tǒng)無論在硬件基礎(chǔ)上還是在軟件算法上都日趨完善,已經(jīng)具備實時性、及時性、高準(zhǔn)確性、低誤報率等優(yōu)質(zhì)性能。新一代智能交通系統(tǒng)通過對路況進行全天候、無間斷監(jiān)測,同時對實時獲取的監(jiān)測數(shù)據(jù)通過計算機技術(shù)進行高效計算處理,以此作為依據(jù)實現(xiàn)道路交通智能化。行人檢測模塊作為智能交通系統(tǒng)中重要的組成部分已經(jīng)成為國內(nèi)外學(xué)者研究的核心課題,在科研領(lǐng)域及工程應(yīng)用方面均具有廣泛的發(fā)展前景。本文針對智能交通系統(tǒng)中的行人檢測部分深入研究,通過對高清攝像機拍攝的行人圖像進行實驗分析。與當(dāng)前流行的行人檢測算法進行實驗對比進而深入分析本文提出的改進算法。在行人檢測領(lǐng)域中,檢測算法方面的工作內(nèi)容主要集中在以下三個方面:一,通過圖像濾波、形態(tài)學(xué)處理等相關(guān)圖像處理技術(shù),對視頻圖像序列進行優(yōu)化處理,在保留圖像有效信息的基礎(chǔ)上盡量減少冗余信息的干擾;二,特征集的選取,本文區(qū)別于傳統(tǒng)特征集的選取,將單一的梯度方向直方圖(Histogram of Oriented Gradient,HOG)特征與局部二值(Local Binary Patterns,LBP)特征利用統(tǒng)計直方圖級聯(lián)進行特征融合,以獲取更深刻更全面的圖像信息;三,在訓(xùn)練分類器的部分,改變傳統(tǒng)的訓(xùn)練方式即利用線性支持向量機(Support Vector Machine,SVM)訓(xùn)練分類器,采用截取后的卷積神經(jīng)網(wǎng)絡(luò)(Convolutional Neural Network,CNN)作為分類器的訓(xùn)練算法,利用三層全連接層進行訓(xùn)練,最終獲取性能較好的分類器。至此,通過對以上三方面的研究與改進,優(yōu)化了算法的實時性與魯棒性。同時也降低了行人背景復(fù)雜時檢測失敗的出現(xiàn)幾率,提高了行人檢測的有效性與可靠性。
[Abstract]:With the continuous development of science and technology, intelligent transportation technology is becoming more and more perfect, and its application scene and demand are expanding. Compared with the traditional intelligent transportation system, The new generation of intelligent transportation system is becoming more and more perfect on the basis of hardware and software algorithm. It has the characteristics of real-time, timeliness and accuracy. The new generation of intelligent transportation systems can monitor the traffic conditions all the time, without interruption. At the same time, the monitoring data obtained in real time can be efficiently calculated and processed by computer technology. As an important part of intelligent transportation system, pedestrian detection module has become the core research topic of domestic and foreign scholars. In the field of scientific research and engineering applications, there is a wide range of development prospects. In this paper, the intelligent transportation system in the pedestrian detection part of in-depth research, Through the experimental analysis of the pedestrian images taken by the high-definition camera, and compared with the current popular pedestrian detection algorithms, the improved algorithm proposed in this paper is analyzed in depth. In the field of pedestrian detection, The work of the detection algorithm is mainly focused on the following three aspects: first, through image filtering, morphological processing and other related image processing technology, the video image sequence is optimized. On the basis of preserving the effective information of the image, the interference of redundant information is reduced as far as possible. Secondly, the selection of feature sets is different from the traditional feature set selection. The histogram of Oriented gradient histogram is fused with the local binary local Binary patterns to obtain more profound and comprehensive image information. Third, in the part of the training classifier, To change the traditional training method, we use linear support Vector machine (SVM) to train classifier, adopt convolutional Neural network (CNN) after intercepting as the training algorithm of classifier, and use three layers full join layer to train. Finally, the classifier with better performance is obtained. By studying and improving the above three aspects, the real-time and robustness of the algorithm is optimized. At the same time, the probability of detection failure is reduced when the pedestrian background is complex. The effectiveness and reliability of pedestrian detection are improved.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;U495

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