本文選題：稀疏表示 + 非負(fù)矩陣分解　； 參考：《大連理工大學(xué)》2015年碩士論文

【摘要】：隨著社會(huì)的快速進(jìn)步和經(jīng)濟(jì)的高速發(fā)展,一、二線城市的機(jī)動(dòng)車數(shù)量成爆炸性逐年增長(zhǎng),從而不可避免地產(chǎn)生了包括交通擁堵等負(fù)面影響,駕駛員如何安全地駕駛也引起了普遍關(guān)注,智能交通應(yīng)運(yùn)而生。而在智能交通系統(tǒng)所涉及的許多計(jì)算機(jī)視覺(jué)(Computer vision)技術(shù)領(lǐng)域當(dāng)中,交通標(biāo)志的定位與識(shí)別又是極其重要的組成部分。交通標(biāo)志的定位與識(shí)別系統(tǒng)目的是在機(jī)動(dòng)車行駛過(guò)程中,快速地搜索交通標(biāo)志然后正確地獲取交通標(biāo)志攜帶的主要信息。因?yàn)樵擃I(lǐng)域具相當(dāng)高的實(shí)用價(jià)值,即提高了駕駛的安全性,因此,多年來(lái)一直是學(xué)者們一個(gè)重要的研究課題。在閱讀了大量相關(guān)論文和其他領(lǐng)域參考文獻(xiàn)后,本文將新的算法應(yīng)用于交通標(biāo)志的定位與識(shí)別系統(tǒng)。本文的創(chuàng)新點(diǎn)包括：(1)增加定位階段的自適應(yīng)性,可以在大部分不同天氣條件下正確定位；(2)基于稀疏分解表示算法,設(shè)計(jì)訓(xùn)練了級(jí)聯(lián)形式的字典,并基于該字典實(shí)現(xiàn)對(duì)交通標(biāo)志的稀疏分解,通過(guò)分解系數(shù)完成交通標(biāo)志的識(shí)別；(3)將非負(fù)矩陣分解的改進(jìn)形式用于圖像分類。每次迭代時(shí)保持字典W不更新,只更新系數(shù)矩陣H,相比稀疏表示算法增加了各部分的物理意義。本文通過(guò)對(duì)實(shí)際拍攝的包含交通標(biāo)志的圖像進(jìn)行標(biāo)志的定位,另外使用德國(guó)交通標(biāo)志(GTSRB)提供的40余種標(biāo)志庫(kù)進(jìn)行訓(xùn)練和識(shí)別測(cè)試,并且與已有算法各性能和特點(diǎn)相比較,結(jié)果表明本論文提出的基于稀疏表示和非負(fù)矩陣分解的兩種創(chuàng)新方法,平衡了實(shí)時(shí)性和識(shí)別率,同時(shí)對(duì)于光照,旋轉(zhuǎn)和遮擋的魯棒性有一定的提升。
[Abstract]:With the rapid progress of the society and the rapid development of the economy, the number of motor vehicles in the first and second tier cities has been explosively increasing year by year, which inevitably has negative effects, including traffic congestion and so on. How to drive safely has also aroused widespread concern, and intelligent transportation has emerged as the times require. The location and recognition of traffic signs is an extremely important part in many fields of computer vision technology involved in Intelligent Transportation system (its). The purpose of the traffic sign location and recognition system is to search the traffic sign quickly and get the main information of the traffic sign correctly. Because this field has a high practical value, that is, to improve the safety of driving, it has been an important research topic for many years. After reading a large number of related papers and other references, this paper applies the new algorithm to the location and recognition system of traffic signs. The innovations of this paper include: (1) increasing the adaptability of the positioning stage, which can be correctly located under most different weather conditions.) based on the sparse decomposition representation algorithm, a dictionary in cascaded form is designed and trained. Based on the dictionary, the sparse decomposition of traffic signs is realized, and the improved form of non-negative matrix decomposition is applied to image classification by using the decomposition coefficient to recognize traffic signs. In each iteration, the dictionary W is not updated, only the coefficient matrix H is updated. Compared with the sparse representation algorithm, the physical meaning of each part is increased. In this paper, we use the more than 40 kinds of sign library provided by GTSRB to train and identify the actual images with traffic signs, and compare them with the performance and characteristics of the existing algorithms. The results show that the proposed two innovative methods based on sparse representation and non-negative matrix decomposition balance real-time and recognition rate, and improve the robustness of illumination, rotation and occlusion.
【學(xué)位授予單位】：大連理工大學(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1條

1 陶工;道路交通標(biāo)志和標(biāo)線實(shí)行新國(guó)標(biāo)[J];道路交通管理;1999年07期

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本文編號(hào)：1795787