本文關(guān)鍵詞： 夜間車輛檢測 閾值分割 Haar特征 車輛跟蹤 自動評價　出處：《北京交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：車輛檢測和跟蹤是智能交通的一個重要內(nèi)容,也為“十二五規(guī)劃”智慧城市建設(shè)的首要目標(biāo)——平安城市提供關(guān)鍵技術(shù)。該領(lǐng)域涉及的運動目標(biāo)檢測和多目標(biāo)跟蹤技術(shù)一直是計算機視覺和機器學(xué)習(xí)的研究熱點。本文在前期調(diào)研工作的基礎(chǔ)上,基于車燈特征對夜間車輛進行檢測,并訓(xùn)練分類器來降低虛警率,在此基礎(chǔ)上實現(xiàn)魯棒的夜間車輛跟蹤。利用自動評價技術(shù)對夜間車輛跟蹤進行自動評價,并根據(jù)結(jié)果提出了改進的方案。 本文的主要工作有： 1、基于車燈特征的車輛檢測技術(shù)的研究。本文首先對比了多種車輛檢測和跟蹤的技術(shù),例如幀間差分法、背景差分法和自適應(yīng)閾值分割法。針對這些傳統(tǒng)方法只適用于白天、光照充足條件下的車輛檢測和跟蹤而不適用于夜間的問題,提出了基于車燈的車輛檢測。在訓(xùn)練階段,采用針對車燈的Haar特征并融合多尺度的幾何、形狀特征進行Adaboost分類器訓(xùn)練。在測試階段,根據(jù)統(tǒng)計直方圖設(shè)定閾值,分割夜間圖像中的車燈。所有檢測到的車燈通過Adaboost分類器分類出正樣本,在此基礎(chǔ)上根據(jù)速度、幾何、形狀等特征的相似性建立數(shù)據(jù)關(guān)聯(lián)進行多目標(biāo)跟蹤。 2、針對車輛跟蹤的自動評價技術(shù)。對車輛跟蹤效果的評價是一個工作量極大的部分,為此,本文提出了一種自動評價的方法來對車輛跟蹤的各項性能進行自動的評價。首先是介紹了一款半自動標(biāo)注工具,來獲取真實的車輛跟蹤結(jié)果。利用真實的車輛跟蹤結(jié)果和車輛跟蹤算法生成的跟蹤結(jié)果通過自動評價技術(shù)自動生成各項評價指標(biāo)。這些評價指標(biāo)包括正確率、缺失率、誤判率和轉(zhuǎn)變率。各項評價指標(biāo)的數(shù)據(jù)顯示,本文提出的方法能夠?qū)囕v進行實時魯棒的跟蹤。
[Abstract]:Vehicle detection and tracking is an important part of the intelligent transportation system, but also for the primary objective of "12th Five-Year plan" wisdom City Construction -- safe city to provide key technology. Tracking technology of moving target detection in the fields and multi targets has been a research focus of computer vision and machine learning. This paper based on the previous research work. On the night, vehicle detection and classifier training based on the characteristics of light, to reduce the false alarm rate, to achieve robust on the basis of the night. On the night of the vehicle tracking vehicle tracking automatic evaluation using automatic evaluation technology, and put forward the improvement scheme according to the results.
The main work of this article is as follows:
1, study the characteristics of vehicle detection technology based on. This paper compares a variety of vehicle detection and tracking technology, such as the inter frame difference method, background difference method and adaptive threshold segmentation method. The traditional method is only suitable for daytime, adequate light under the condition of vehicle detection and tracking is not suitable for the night, the lights of the vehicle detection based on. During the training phase, the geometry for Haar lights and feature fusion of multi-scale, shape feature Adaboost classifier training. During the testing phase, according to the statistics histogram set the threshold segmentation in the image, the night lights. All the detected light by Adaboost classifier classification the positive samples, based on the speed, geometry, shape similarity and other characteristics of the establishment of Data Association for multiple target tracking.
2, according to the automatic evaluation technology of vehicle tracking. To evaluate the effect of vehicle tracking is a great part of a workload for this purpose, this paper presents a method for automatic evaluation to the evaluation of the performance of automatic vehicle tracking. At first it introduces a semi-automatic annotation tool to obtain real tracking results the use of vehicles. Vehicle tracking and generate results of real vehicle tracking algorithm based on tracking results through the automatic evaluation technology of automatic generation of evaluation indexes. These indexes include the correct rate, loss rate, error rate and transformation rate. The evaluation index data show that the proposed method can real-time robust vehicle tracking.

【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;TP391.41

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本文編號：1555347