本文選題：安全輔助駕駛系統(tǒng) + 車輛檢測��； 參考：《合肥工業(yè)大學》2015年碩士論文

【摘要】：隨著公路交通事業(yè)的迅速發(fā)展,給人們的生活帶來便利的同時,也造成了道路交通安全問題日益突出。安全輔助駕駛系統(tǒng)作為智能交通系統(tǒng)的重要組成部分,能夠有效地預防交通事故的發(fā)生,提高行車的安全性。而前方車輛檢測與跟蹤是安全輔助駕駛系統(tǒng)的核心環(huán)節(jié),為車輛的信息提取以及行為分析提供了重要的保證。本文在分析和比較國內(nèi)外各種算法的基礎上,研究并提出了相應的基于單目視覺的車輛檢測與跟蹤算法。全文主要內(nèi)容如下：1)對前方車輛檢測與跟蹤的研究背景和意義進行了探討,闡述了常見的基于單目視覺的前方車輛檢測與跟蹤方法,并分析了這些方法的優(yōu)缺點,為后續(xù)研究作準備。2)研究了基于AdaBoost的前方車輛檢測算法。選取Haar-like特征作為圖像特征,利用Gentle AdaBoost算法和CasCade算法對訓練樣本進行離線學習,得到級聯(lián)結構的車輛分類器；檢測過程中,采用等比放大檢測窗口的方式掃描待檢圖像,利用車輛分類器對檢測窗口進行分類,最后綜合各個檢測窗口的結果,得出車輛的最終位置。實驗結果表明,該方法能夠有效地檢測出前方車輛,具有一定的魯棒性,基本上滿足實時性要求。3)提出一種基于改進TLD的前方車輛跟蹤算法。TLD算法是一種新穎的目標跟蹤算法,在給定極少的先驗知識的情況下,能夠迅速地學習目標特征并進行有效的跟蹤。而車輛的先驗知識可由前方車輛檢測算法提供,因此,TLD跟蹤算法完全能夠適用前方車輛跟蹤問題上。然而,TLD跟蹤模塊均勻地選取特征點進行跟蹤,無法保證所選特征點被可靠地跟蹤。針對這個問題,提出一種基于關鍵特征點的選取方式,保證所選特征點能夠被正確可靠地跟蹤,防止跟蹤發(fā)生漂移,提高跟蹤精度。另一方面,在TLD檢測模塊中引入了基于軌跡連續(xù)性的在線位置預測,在保證正確跟蹤的前提下,縮小了檢測范圍,提高了運算速度。最后,利用改進的TLD算法對前方車輛進行跟蹤。實驗結果表明,該算法能夠有效的對前方車輛跟蹤,且在各種較難處理的情況下具有較好的跟蹤效果。
[Abstract]:With the rapid development of highway traffic, it brings convenience to people's life, but also causes the problem of road traffic safety more and more prominent. As an important part of intelligent transportation system, safety assistant driving system can effectively prevent traffic accidents and improve the safety of traffic. The detection and tracking of vehicle in front is the core of the safety assistant driving system, which provides an important guarantee for the information extraction and behavior analysis of the vehicle. Based on the analysis and comparison of various algorithms at home and abroad, this paper studies and proposes the corresponding vehicle detection and tracking algorithm based on monocular vision. The main contents of this paper are as follows: (1) the research background and significance of forward vehicle detection and tracking are discussed, and the common methods of forward vehicle detection and tracking based on monocular vision are expounded, and the advantages and disadvantages of these methods are analyzed. The forward vehicle detection algorithm based on AdaBoost is studied. Haar-like feature is selected as image feature, Gentle AdaBoost algorithm and CasCade algorithm are used to study the training sample offline, and the cascade structure vehicle classifier is obtained. In the process of detection, the image is scanned by equal ratio amplification detection window. The detection window is classified by vehicle classifier and the final position of the vehicle is obtained by synthesizing the results of each detection window. The experimental results show that the proposed method can detect the vehicle in front effectively, which is robust, and basically meets the real-time requirement. 3) A novel target tracking algorithm based on improved TLD is proposed. Given minimal prior knowledge, we can quickly learn target features and track them effectively. The prior knowledge of the vehicle can be provided by the forward vehicle detection algorithm, so the TLD tracking algorithm is fully applicable to the forward vehicle tracking problem. However, the TLD tracking module selects the feature points uniformly and can not guarantee that the selected feature points can be tracked reliably. To solve this problem, a method based on key feature points is proposed to ensure that the selected feature points can be tracked correctly and reliably, to prevent the drift of tracking, and to improve the tracking accuracy. On the other hand, the on-line position prediction based on trajectory continuity is introduced into the TLD detection module, which reduces the detection range and improves the operation speed on the premise of correct tracking. Finally, the improved TLD algorithm is used to track forward vehicles. The experimental results show that the algorithm can track the vehicle in front effectively and has a good tracking effect under various difficult cases.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495;TP391.41

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