發(fā)布時(shí)間：2018-04-15 00:10

  本文選題：信號(hào)燈識(shí)別 + 目標(biāo)跟蹤��； 參考：《北京理工大學(xué)》2015年碩士論文

【摘要】：交通信號(hào)燈識(shí)別是無(wú)人駕駛技術(shù)的一個(gè)基本組成部分。本課題的研究?jī)?nèi)容是基于無(wú)人駕駛車輛平臺(tái)上豐富的傳感器與信息資源，系統(tǒng)地設(shè)計(jì)一套包括了檢測(cè)、跟蹤、定位、識(shí)別功能的交通信號(hào)燈識(shí)別系統(tǒng)。 本課題設(shè)計(jì)的信號(hào)燈識(shí)別系統(tǒng)所融合的傳感器及信息包括：車載相機(jī)所采集的圖像序列，全球定位系統(tǒng)/慣導(dǎo)組合導(dǎo)航系統(tǒng)（即GPS/INS組合導(dǎo)航系統(tǒng)）所測(cè)量的，或者通過(guò)激光雷達(dá)和即時(shí)定位與地圖構(gòu)建算法（即SLAM算法）所獲得的車輛平臺(tái)位置與航向。 本課題中，跟蹤模塊將處理圖像序列的檢測(cè)器的輸出看作是交通信號(hào)燈目標(biāo)的受干擾帶噪聲觀測(cè)，而且考慮到檢測(cè)所得的候選區(qū)域可能出現(xiàn)誤檢與漏檢的情況。跟蹤器利用多目標(biāo)跟蹤算法中的數(shù)據(jù)關(guān)聯(lián)對(duì)這些觀測(cè)進(jìn)行處理，從雜亂的觀測(cè)中提取來(lái)自信號(hào)燈的真實(shí)候選區(qū)域并剔除誤檢的區(qū)域。經(jīng)過(guò)數(shù)據(jù)關(guān)聯(lián)后的穩(wěn)定候選區(qū)域?qū)?huì)經(jīng)過(guò)分類器，識(shí)別信號(hào)燈的圖案。在這個(gè)總的流程之下，為了提高多目標(biāo)跟蹤的性能，本課題對(duì)信號(hào)燈這一特殊目標(biāo)的運(yùn)動(dòng)模型做了比較深入的研究，設(shè)計(jì)了運(yùn)動(dòng)預(yù)測(cè)算法。在車輛平臺(tái)接近路口時(shí)，信號(hào)燈的三維位置對(duì)運(yùn)動(dòng)模型有較大影響，所以課題又設(shè)計(jì)了基于多視角觀測(cè)的交通信號(hào)燈定位算法。而跟蹤與定位的準(zhǔn)確性又與車輛自身的位置、姿態(tài)測(cè)量相關(guān)，為了彌補(bǔ)GPS/INS組合導(dǎo)航系統(tǒng)的缺陷，，課題又對(duì)非外源式的SLAM算法進(jìn)行了研究，提出來(lái)一種基于圖像處理的SLAM算法。此外，由于該系統(tǒng)融合了來(lái)自多個(gè)不同傳感器的多種數(shù)據(jù)，所以也對(duì)傳感器間的時(shí)間、空間對(duì)準(zhǔn)問(wèn)題作了研究。
[Abstract]:Traffic signal recognition is a basic component of driverless technology.Based on the abundant sensors and information resources on the driverless vehicle platform, this paper systematically designs a set of traffic signal light recognition system which includes detection, tracking, positioning and recognition functions.The sensors and information of the signal lamp recognition system designed in this paper include: the image sequence collected by the vehicle camera, the global positioning system (GPS) / inertial navigation integrated navigation system (GPS/INS integrated navigation system),Or the vehicle platform position and course can be obtained by lidar and real-time location and map construction algorithm (i.e. SLAM algorithm).In this thesis, the tracking module regards the output of the detector which processes image sequence as the noise observation of the target of traffic signal light, and considers that the candidate region of the detection may appear the case of false detection and miss detection.The tracker uses the data association in the multi-target tracking algorithm to process these observations, and extracts the true candidate regions from the signal lights from the clutter observations and removes the areas of false detection.After data association, the stable candidate area will be identified by classifier to recognize the pattern of the signal light.In this general process, in order to improve the performance of multi-target tracking, the motion model of signal lamp is studied deeply, and the motion prediction algorithm is designed.When the vehicle platform is near the intersection, the three-dimensional position of the signal light has great influence on the motion model, so the algorithm of the traffic signal lamp location based on multi-angle observation is designed.The accuracy of tracking and positioning is related to the position and attitude measurement of the vehicle itself. In order to make up for the defects of GPS/INS integrated navigation system, the non-exogenous SLAM algorithm is studied, and a SLAM algorithm based on image processing is proposed.In addition, the time and space alignment between sensors is also studied because the system integrates many kinds of data from many different sensors.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 趙文華,姚天翔,葉秀清,顧偉康;RANSAC算法在視頻去抖動(dòng)中的應(yīng)用[J];電路與系統(tǒng)學(xué)報(bào);2005年04期

2 何海濤;宋健;;基于鏈碼趨勢(shì)表的交通燈形狀識(shí)別方法[J];計(jì)算機(jī)工程;2011年15期

3 徐成;譚乃強(qiáng);劉彥;;基于Lab色彩空間和模板匹配的實(shí)時(shí)交通燈識(shí)別算法[J];計(jì)算機(jī)應(yīng)用;2010年05期

4 徐麗珍;孫霖;;基于AdaBoost的人行橫道紅綠燈自動(dòng)識(shí)別算法[J];計(jì)算機(jī)應(yīng)用與軟件;2010年03期

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