發(fā)布時(shí)間：2018-08-24 15:02

【摘要】：隨著城市道路交通管控的需要，道路交通信息的相對(duì)匱乏正促使著交通信息全面感知技術(shù)的快速發(fā)展。在自由流狀態(tài)下，對(duì)當(dāng)前某一特定車輛的精準(zhǔn)識(shí)別是交通感知領(lǐng)域的一個(gè)重大研究課題，如何借鑒人類大腦的認(rèn)知和融合機(jī)理，利用多機(jī)制傳感信息，有效融合并識(shí)別當(dāng)前指定車輛，從而達(dá)到精準(zhǔn)無(wú)誤的效果，已成為車輛自動(dòng)識(shí)別的一個(gè)研究熱點(diǎn)。 本文在充分研究和總結(jié)車輛視頻識(shí)別、射頻識(shí)別、電感傳感識(shí)別、模糊理論及多源信息融合的研究現(xiàn)狀和基本理論的基礎(chǔ)上，提出了基于嵌入式雙核架構(gòu)的車輛多機(jī)制識(shí)別應(yīng)用模型，并對(duì)車輛多機(jī)制融合識(shí)別算法、復(fù)雜情況下的車牌定位算法及嵌入式QT平臺(tái)的車牌識(shí)別系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)等關(guān)鍵問(wèn)題進(jìn)行了深入研究。主要研究工作包括： （1）提出了基于證據(jù)理論的多特征融合車牌定位算法。該算法通過(guò)對(duì)多個(gè)候選車牌區(qū)域分別提取寬高比、紋理密度和色調(diào)主值等顯著性特征，，通過(guò)證據(jù)理論融合各特征置信度，識(shí)別出真正的車牌區(qū)域，最終實(shí)現(xiàn)車牌定位，更加適應(yīng)復(fù)雜情況下的車牌定位。 （2）提出了基于模糊理論和證據(jù)理論的多機(jī)制傳感信息融合車輛識(shí)別算法。該算法通過(guò)提取由視頻、射頻及電感傳感器識(shí)別的車輛特征信息，采用基于貼近度的模糊識(shí)別算法求出待識(shí)車輛對(duì)標(biāo)準(zhǔn)模式車輛的相似度，并將其作為各識(shí)別機(jī)制的基本概率分配，送往信息融合中心，通過(guò)證據(jù)理論進(jìn)一步融合，最終輸出融合后的目標(biāo)車輛識(shí)別結(jié)果，克服了單一車輛識(shí)別機(jī)制存在的固有缺陷。 （3）完成了基于嵌入式QT的車牌識(shí)別系統(tǒng)的設(shè)計(jì)。以Linux為平臺(tái)，以QT為開發(fā)工具，實(shí)現(xiàn)了ARM嵌入式平臺(tái)抓拍圖像的讀取顯示，灰度變換，二值化、膨脹腐蝕、平滑濾波、邊緣提取等相關(guān)圖像處理操作，最終實(shí)現(xiàn)車牌定位功能。 仿真實(shí)驗(yàn)表明，所提出的算法是可行的�；谧C據(jù)理論的多特征融合車牌定位算法明顯優(yōu)于單特征定位效果，提高了車牌定位準(zhǔn)確率；基于模糊理論和證據(jù)理論的多機(jī)制傳感信息融合車輛識(shí)別算法能夠給出可信度更高的識(shí)別結(jié)果。
[Abstract]:With the need of urban road traffic control, the relative lack of road traffic information is promoting the rapid development of comprehensive traffic information perception technology. In the condition of free flow, the accurate recognition of a particular vehicle is an important research topic in the field of traffic perception. How to use the mechanism of cognition and fusion of human brain for reference and use multi-mechanism sensing information for reference is an important research topic in the field of traffic perception. Effective fusion and recognition of current designated vehicles, so as to achieve accurate results, has become a research hotspot in automatic vehicle recognition. In this paper, the research status and basic theories of vehicle video identification, radio frequency identification, inductance sensor identification, fuzzy theory and multi-source information fusion are fully studied and summarized. An application model of vehicle multi-mechanism recognition based on embedded dual-core architecture is proposed, and the vehicle multi-mechanism fusion recognition algorithm is proposed. The key problems such as the algorithm of license plate location and the design and implementation of the vehicle license plate recognition system based on embedded QT platform are studied in detail. The main research work includes: (1) A multi-feature fusion license plate location algorithm based on evidence theory is proposed. The algorithm extracts salient features such as ratio of width to height texture density and color principal value from several candidate license plate regions. Through the evidence theory fusion of each feature confidence the true license plate region can be recognized and finally the license plate location can be realized. It is more suitable for vehicle license plate location in complex situations. (2) A multi-mechanism sensor information fusion vehicle recognition algorithm based on fuzzy theory and evidence theory is proposed. By extracting the vehicle feature information identified by video, radio frequency and inductance sensors, the fuzzy recognition algorithm based on closeness degree is used to obtain the similarity of vehicles to standard mode vehicles. As the basic probability distribution of each recognition mechanism, it is sent to the information fusion center, and finally the target vehicle recognition result is output through the further fusion of the evidence theory. It overcomes the inherent defects of single vehicle recognition mechanism. (3) the design of vehicle license plate recognition system based on embedded QT is completed. With Linux as the platform and QT as the development tool, the image reading and displaying, gray level transformation, binarization, expansion corrosion, smoothing filtering, edge extraction and other related image processing operations are realized on the ARM embedded platform. Finally, the license plate location function is realized. Simulation results show that the proposed algorithm is feasible. The multi-feature fusion license plate location algorithm based on evidence theory is obviously superior to the single feature location algorithm, and improves the accuracy of license plate location. The multi-mechanism sensor information fusion vehicle recognition algorithm based on fuzzy theory and evidence theory can give a more reliable recognition result.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.41;TP368.1

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 秦悅;BTM測(cè)試管理系統(tǒng)與測(cè)試關(guān)鍵技術(shù)的研究[D];北京交通大學(xué);2013年

本文編號(hào)：2201193