本文選題：機器視覺 + 雙核并行DSP��； 參考：《長安大學》2013年博士論文

【摘要】：隨著我國公路交通運輸業(yè)快速發(fā)展的同時，道路交通安全問題日益突出，公路客運事故一般都是人員死傷慘重的惡性事故，不僅給運輸企業(yè)造成巨大的經(jīng)濟損失，而且給當?shù)毓愤\輸管理部門造成了極壞的社會影響，甚至成為了新的社會不穩(wěn)定因素。因此，開展客運車輛危險行駛狀態(tài)機器視覺辨識系統(tǒng)的研究，有助于改善我國公路客運安全性和提高公路客運安全管理能力，并能夠?qū)Πl(fā)生交通事故之后的責任認定提供部分可視化證據(jù)，具有廣闊的應(yīng)用前景和市場需求。 本文依托“十一五”國家科技支撐計劃重大項目(2009BAG13A07)和國家自然科學基金項目(51278062)，綜合運用計算機圖形學、信息工程學、車輛工程學、交通工程學等多學科理論以及機器視覺技術(shù)中的車載CCD視覺傳感采集技術(shù)、嵌入式雙核并行高速DSP數(shù)字圖像處理技術(shù)、邊緣形狀檢測與分析技術(shù)、機器學習技術(shù)與模式識別技術(shù)，通過大量模擬試驗、數(shù)據(jù)分析、理論建模和程序設(shè)計，研究能夠?qū)崟r采集客運車輛行駛狀態(tài)視覺圖像信息，在線辨識客運車輛行駛過程中存在的潛在危險，適時警示和記錄駕駛?cè)朔钦ｑ{駛行為的客運車輛危險行駛狀態(tài)機器視覺辨識技術(shù)及其實現(xiàn)系統(tǒng)。 針對客運車輛行駛狀態(tài)、運行軌跡和道路環(huán)境的視覺感知問題，采用多目標特征集合的方法，進行了道路標識線方位與線型識別以及車輛橫向偏航警告技術(shù)的研究。通過對道路圖像灰度均衡化增強、快速重組中值濾波、Scharr濾波邊緣信息提取、感興趣區(qū)域搜索和約束塊掃描式最優(yōu)閾值分割處理，深度挖掘道路邊緣輪廓信息�；诜N子點投票區(qū)域約束、極角區(qū)域約束以及鏈碼方向約束等邊界約束條件，對Hough變換進行改進并實現(xiàn)了道路標識線的方位檢測；融合HSI色彩空間分割與動態(tài)窗口搜索實現(xiàn)了道路標識線線型的辨識；引入?yún)^(qū)域約束粒子濾波跟蹤模型，提高了道路標識線的檢測效率和環(huán)境適應(yīng)能力。依據(jù)逆透視投影變換重建道路關(guān)鍵信息，預測車道平面內(nèi)自車的行駛軌跡，充分考慮自車橫向分速率和橫向偏航角的影響，在空間域和時間域內(nèi)量化危險度，建立了基于自車位姿與時域危險度的車輛橫向偏航警告模型，改善了系統(tǒng)的警告機制，提高了系統(tǒng)的可接受度。 針對前方車輛圖像識別過程中存在的干擾因素較多、復雜背景排除困難和單一特征表示的局限性等問題，采用多尺度方向特征提取的方法，，進行了同車道內(nèi)自車前方的目標車輛圖像識別技術(shù)的研究。充分挖掘前方車輛圖像信息設(shè)置目標搜索區(qū)域，減小了系統(tǒng)運算處理信息量。通過對路面灰度均值突變特征的分析，提出前方車輛存在性假設(shè)；利用雙通道Gabor濾波器提取車輛灰度樣本的多尺度方向特征，融合Adaboost分類器對提取的特征樣本進行學習訓練分類，確定前方車輛在圖像中的位置；依據(jù)信息熵歸一化對稱性測度，驗證前方車輛存在性假設(shè)，排除虛假目標；通過車輛特征樣本的離線訓練與在線檢測相結(jié)合的機器學習方式，實現(xiàn)了前方車輛快速、準確的識別和定位。融合改進GM(1,1)灰色預測模型，利用少量歷史數(shù)據(jù)信息動態(tài)預測前方車輛的運動軌跡，并以幀間連續(xù)性為線索，建立了一種檢測與跟蹤反饋工作機制，緩和了目標車輛檢測過程中魯棒性與實時性之間的矛盾。 在前方車輛圖像識別定位的基礎(chǔ)上，采用人-車-路多源信息融合的方法，對安全車距預警技術(shù)進行了深入研究。通過對單目視覺測距原理的研究分析，在CCD視覺傳感器關(guān)鍵測距參數(shù)精確標定的基礎(chǔ)上，建立了基于車道平面約束的單目視覺縱向車距測量模型，實現(xiàn)了縱向車距的精確測量。充分考慮駕駛?cè)苏J知響應(yīng)特征、車輛響應(yīng)特性和道路環(huán)境等因素，運用多傳感器信息融合技術(shù)獲取前車及自車的行駛狀態(tài)信息，建立了基于人-車-路多源信息融合的安全車距模型。以駕駛?cè)藨?yīng)急響應(yīng)概率智能體、前車與自車相對行駛狀態(tài)智能體和道路環(huán)境約束智能體互相協(xié)作為架構(gòu)，建立了群智能體協(xié)作的安全車距預警模型，通過模糊積分與模糊測度進行預警決策，充分考慮了外界不確定性因素的影響，在保證行車安全的同時兼顧了道路的通行能力。 探討了客運車輛危險行駛狀態(tài)機器視覺辨識系統(tǒng)的總體設(shè)計與實現(xiàn)，以嵌入式雙核并行高速數(shù)字圖像信號處理DSP和微處理器MCU作為硬件開發(fā)平臺，完成了系統(tǒng)關(guān)鍵部件的選型以及總體功能模塊的設(shè)計，并對系統(tǒng)圖像處理過程中的內(nèi)存分配和調(diào)用進行了優(yōu)化設(shè)計。
[Abstract]:Along with the rapid development of the highway transportation industry in our country, the problem of road traffic safety is becoming more and more prominent. The highway passenger traffic accidents are usually fatal and serious accidents, which not only cause huge economic losses to the transportation enterprises, but also make a very bad social impact on the local highway transportation management department, and even become a new society. Will the unstable factors. Therefore, the research on machine vision identification system to carry out the passenger vehicle danger, help to improve the safety of our country's highway passenger transportation and improve the ability of highway passenger traffic safety management, and after the traffic accident liability provides visual evidence, has broad application prospects and market demand.
Based on the "11th Five-Year" National Science and technology support program (2009BAG13A07) and the National Natural Science Foundation (51278062), the multi-disciplinary theory of computer graphics, information engineering, vehicle engineering, traffic engineering, and vehicle CCD visual sensing acquisition technology in machine vision technology, and embedded dual core are used in this paper. The high-speed DSP digital image processing technology, edge shape detection and analysis technology, machine learning technology and pattern recognition technology, through a large number of simulation experiments, data analysis, theoretical modeling and programming, can be used to collect real-time visual image information of passenger vehicle running state, and identify the potential of passenger vehicle in the process of running on line. A machine vision identification technology and its implementation system for dangerous driving state of passenger vehicles timely warning and recording abnormal driving behavior of drivers.
In view of the visual perception of the running state, the running track and the road environment of the passenger vehicle, the multi target feature set method is adopted to carry out the research on the identification of the road marking line and the line type and the lateral yaw warning technology of the vehicle. By strengthening the gray balance of the road image, the median filtering is quickly reorganized and the Scharr filter edge signal is filtered. Information extraction, region of interest search and constrained block scan optimal threshold segmentation processing, depth mining of road edge contour information. Based on the boundary constraints such as seed point voting area constraint, polar region constraint and chain code direction constraint, the Hough transformation is improved and the orientation detection of road identification line is realized; HSI color is fused. Spatial segmentation and dynamic window search are used to identify the line pattern of road identification line, and the region constrained particle filter tracking model is introduced to improve the detection efficiency and environmental adaptability of the road identification line. The road key information is reconstructed based on inverse perspective projection transformation, and the driving trajectory of the car in the Lane plane is predicted, and the vehicle crosswise is fully considered. The risk degree is quantified in space and time domain, and the vehicle lateral deviation warning model based on self position and time domain risk is established. The warning mechanism of the system is improved and the acceptability of the system is improved.
In view of the many interference factors in the process of image recognition in front of the vehicle, the difficulty of the complex background elimination and the limitation of the single feature representation, the method of multi-scale directional feature extraction is adopted to study the image recognition technology of the target vehicle in front of the same lane. The standard search area reduces the amount of information in the processing of the system. Through the analysis of the abrupt change characteristics of the mean value of the road surface, the existence hypothesis of the vehicle ahead is proposed. The multi scale direction feature of the vehicle gray sample is extracted with the dual channel Gabor filter, and the learning and training classification of the extracted feature samples is made by the fusion of Adaboost classifier. The location of the square vehicle in the image; based on the entropy normalization of the symmetry measure to verify the existence hypothesis of the vehicle ahead and eliminate the false targets; through the off-line training of the vehicle characteristic samples and the machine learning method combined with on-line detection, the fast, accurate recognition and positioning of the vehicle ahead are realized. The fusion improved GM (1,1) grey prediction is achieved. In this model, a small amount of historical data is used to dynamically predict the motion trajectory of the vehicle ahead, and a detection and tracking feedback mechanism is established with the interframe continuity as a clue, which alleviated the contradiction between the robustness and the real-time performance of the target vehicle detection process.
On the basis of the vehicle image recognition and location ahead, the method of human vehicle road multi source information fusion is used to study the safe distance warning technology. Through the study and analysis of the principle of monocular vision distance measurement, the single vision based on the lane plane constraint is established on the basis of the accurate calibration of the key distance parameters of the CCD vision sensor. The longitudinal vehicle distance measurement model is used to realize the accurate measurement of the longitudinal distance. Considering the driver's cognitive response characteristics, the vehicle response characteristics and the road environment, the multi-sensor information fusion technology is used to obtain the driving state information of the car and the car, and a safe distance model based on the fusion of human vehicle and road multi source information is established. The driving person emergency response probability agent, the relative driving state agent of the front car and the self vehicle and the road environment constraint agent are used as the framework, and the safe distance warning model of the group agent is established, and the early warning decision is carried out through fuzzy integral and fuzzy measure, which fully considers the influence of the external uncertainty factors and ensures the driving. It is safe to take into account the capacity of the road.
The overall design and implementation of the machine vision identification system for the dangerous driving state of passenger vehicles is discussed. The embedded dual core parallel high-speed digital image signal processing DSP and the microprocessor MCU are used as the hardware development platform, and the key components of the system are selected and the overall functional modules are designed. The storage allocation and call are optimized.
【學位授予單位】：長安大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：U492.8

【引證文獻】

相關(guān)博士學位論文 前7條

1 肖獻強;基于信息融合的駕駛行為識別關(guān)鍵技術(shù)研究[D];合肥工業(yè)大學;2011年

2 林廣宇;基于嵌入式技術(shù)的車載圖像監(jiān)控系統(tǒng)研究[D];長安大學;2009年

3 張良力;面向安全預警的機動車駕駛意圖識別方法研究[D];武漢理工大學;2011年

4 陳軍;基于DSP的高速公路車道偏離報警系統(tǒng)研究[D];天津大學;2010年

5 沈\

本文編號：2027735