本文選題：信號燈識別 + 目標跟蹤�。� 參考：《北京理工大學(xué)》2015年碩士論文

【摘要】：交通信號燈識別是無人駕駛技術(shù)的一個基本組成部分。本課題的研究內(nèi)容是基于無人駕駛車輛平臺上豐富的傳感器與信息資源，系統(tǒng)地設(shè)計一套包括了檢測、跟蹤、定位、識別功能的交通信號燈識別系統(tǒng)。 本課題設(shè)計的信號燈識別系統(tǒng)所融合的傳感器及信息包括：車載相機所采集的圖像序列，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)）所測量的，或者通過激光雷達和即時定位與地圖構(gòu)建算法（即SLAM算法）所獲得的車輛平臺位置與航向。 本課題中，跟蹤模塊將處理圖像序列的檢測器的輸出看作是交通信號燈目標的受干擾帶噪聲觀測，而且考慮到檢測所得的候選區(qū)域可能出現(xiàn)誤檢與漏檢的情況。跟蹤器利用多目標跟蹤算法中的數(shù)據(jù)關(guān)聯(lián)對這些觀測進行處理，從雜亂的觀測中提取來自信號燈的真實候選區(qū)域并剔除誤檢的區(qū)域。經(jīng)過數(shù)據(jù)關(guān)聯(lián)后的穩(wěn)定候選區(qū)域?qū)?jīng)過分類器，識別信號燈的圖案。在這個總的流程之下，為了提高多目標跟蹤的性能，本課題對信號燈這一特殊目標的運動模型做了比較深入的研究，設(shè)計了運動預(yù)測算法。在車輛平臺接近路口時，信號燈的三維位置對運動模型有較大影響，所以課題又設(shè)計了基于多視角觀測的交通信號燈定位算法。而跟蹤與定位的準確性又與車輛自身的位置、姿態(tài)測量相關(guān)，為了彌補GPS/INS組合導(dǎo)航系統(tǒng)的缺陷，，課題又對非外源式的SLAM算法進行了研究，提出來一種基于圖像處理的SLAM算法。此外，由于該系統(tǒng)融合了來自多個不同傳感器的多種數(shù)據(jù)，所以也對傳感器間的時間、空間對準問題作了研究。
[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)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495;TP391.41

【參考文獻】

相關(guān)期刊論文 前4條

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

2 何海濤;宋健;;基于鏈碼趨勢表的交通燈形狀識別方法[J];計算機工程;2011年15期

3 徐成;譚乃強;劉彥;;基于Lab色彩空間和模板匹配的實時交通燈識別算法[J];計算機應(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識別算法[J];計算機應(yīng)用與軟件;2010年03期

本文編號：1751631