發(fā)布時(shí)間：2018-06-17 19:37

  本文選題：視覺(jué)車(chē)輛檢測(cè) + 視覺(jué)車(chē)輛跟蹤　； 參考：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年碩士論文

【摘要】：隨著汽車(chē)保有量的迅猛增長(zhǎng),公路交通事故已經(jīng)成為全球范圍內(nèi)日趨嚴(yán)重的公共安全問(wèn)題,亟待解決。前碰撞預(yù)警系統(tǒng)是智能輔助駕駛系統(tǒng)的重要組成部分,能有效降低公路交通事故發(fā)生的概率。車(chē)輛檢測(cè)和跟蹤的準(zhǔn)確性、連續(xù)性和實(shí)時(shí)性是影響該系統(tǒng)功能發(fā)揮的決定性因素。其中,車(chē)輛定位的準(zhǔn)確性和連續(xù)性是預(yù)警功能的前提,而實(shí)時(shí)性是預(yù)警功能有效發(fā)揮的關(guān)鍵,能使駕駛者及早發(fā)現(xiàn)險(xiǎn)情。因此本文致力于車(chē)載單目視覺(jué)的車(chē)輛檢測(cè)與跟蹤算法研究,具體研究?jī)?nèi)容如下:基于主動(dòng)學(xué)習(xí)的分類(lèi)器模型訓(xùn)練�；跈C(jī)器學(xué)習(xí)的視覺(jué)車(chē)輛檢測(cè)需要大量帶有標(biāo)簽的樣本數(shù)據(jù),用以訓(xùn)練出能夠準(zhǔn)確分類(lèi)圖像中車(chē)輛與背景的分類(lèi)器模型。本文提出一種基于錯(cuò)誤分類(lèi)樣本抽樣策略的主動(dòng)學(xué)習(xí)算法,以較小的人工標(biāo)注成本獲得最具信息量的樣本數(shù)據(jù),迭代訓(xùn)練優(yōu)化分類(lèi)器的性能。Adaboost(Adaptive Boosting)級(jí)聯(lián)多目標(biāo)車(chē)輛檢測(cè)。為了提高車(chē)輛檢測(cè)的準(zhǔn)確性,本文提出一種分區(qū)域多分類(lèi)器車(chē)輛檢測(cè)方法。根據(jù)車(chē)輛特征在檢測(cè)視野中的差異,把待檢測(cè)車(chē)輛分類(lèi)為前向車(chē)輛、左斜側(cè)向車(chē)輛和右斜側(cè)向車(chē)輛,分別訓(xùn)練級(jí)聯(lián)分類(lèi)器進(jìn)行檢測(cè)。同時(shí),為了提高車(chē)輛檢測(cè)速度,提出一種結(jié)合相機(jī)標(biāo)定的多分辨率加速車(chē)輛檢測(cè)算法,對(duì)檢測(cè)視野中遠(yuǎn)近不同的車(chē)輛采用不同程度的圖像降采樣分別檢測(cè)。HOG(Histogram of Oriented Gradients)特征跟蹤與 Adaboost 檢測(cè)融合。針對(duì)Adaboost級(jí)聯(lián)車(chē)輛檢測(cè)結(jié)果不夠連續(xù)的問(wèn)題,提出一種Adaboost級(jí)聯(lián)檢測(cè)與HOG特征跟蹤相互融合的車(chē)輛檢測(cè)跟蹤算法。通過(guò)HOG特征跟蹤的融入,提高了約10%的車(chē)輛檢測(cè)率,使檢測(cè)結(jié)果更加連續(xù)。前碰撞預(yù)警系統(tǒng)設(shè)計(jì)實(shí)現(xiàn)。文章最后應(yīng)用本文研究的車(chē)輛檢測(cè)跟蹤算法設(shè)計(jì)出一套前碰撞預(yù)警系統(tǒng),通過(guò)真實(shí)交通場(chǎng)景測(cè)試,該系統(tǒng)可以實(shí)時(shí)、準(zhǔn)確和連續(xù)的檢測(cè)跟蹤前方車(chē)輛并計(jì)算與其距離,實(shí)時(shí)監(jiān)控前方潛在的碰撞危險(xiǎn),及時(shí)發(fā)出預(yù)警信號(hào),從而避免交通事故的發(fā)生。
[Abstract]:With the rapid growth of vehicle ownership, road traffic accidents have become more and more serious public safety problems all over the world. Pre-collision warning system is an important part of intelligent auxiliary driving system, which can effectively reduce the probability of road traffic accidents. The accuracy, continuity and real-time of vehicle detection and tracking are the decisive factors affecting the function of the system. Among them, the accuracy and continuity of vehicle positioning is the premise of early warning function, and real-time is the key to the effective use of early warning function, which can enable the driver to detect the danger as early as possible. Therefore, this paper focuses on vehicle detection and tracking algorithm based on vehicle monocular vision. The research contents are as follows: classifier model training based on active learning. Visual vehicle detection based on machine learning requires a large number of labeled sample data to train a classifier model that can accurately classify vehicles and backgrounds in images. In this paper, an active learning algorithm based on sample sampling strategy for error classification is proposed to obtain the most informative sample data at a lower cost of manual annotation, and iterative training to optimize the performance of the classifier. Adaboosting Adaptive boost) cascade multi-objective vehicle detection. In order to improve the accuracy of vehicle detection, this paper presents a multi-classifier vehicle detection method. According to the difference of vehicle characteristics in the field of vision, the vehicles to be tested are classified as forward vehicles, left oblique vehicles and right oblique vehicles, and the cascaded classifiers are trained for detection. At the same time, in order to improve the speed of vehicle detection, a multi-resolution accelerated vehicle detection algorithm combined with camera calibration is proposed. Different degrees of image demotion were used to detect different vehicles in the visual field. The feature tracking of the histogram of oriented radientsand the fusion of Adaboost detection were used respectively. In order to solve the problem that the detection results of Adaboost cascaded vehicles are not continuous, a vehicle detection and tracking algorithm based on Adaboost cascade detection and hog feature tracking is proposed. By means of HOG feature tracking, the vehicle detection rate is increased by about 10%, and the detection results are more continuous. Design and implementation of pre-collision warning system. Finally, using the vehicle detection and tracking algorithm studied in this paper, a pre-collision warning system is designed. Through the real traffic scene test, the system can detect and track the vehicle in front of the vehicle in real time, accurately and continuously, and calculate the distance between the vehicle and the vehicle. Real-time monitoring of potential collision hazards ahead, timely warning signals to avoid traffic accidents.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U463.6;TP391.41

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本文編號(hào)：2032219