基於卷積神經網路之市區道路場景自適應車輛偵測機制

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姓名

楊道偉(Dao-Wei Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於卷積神經網路之市區道路場景自適應車輛偵測機制
(An Adaptive Vehicle Detection Scheme for Urban Traffic Scenes based on Convolutional Neural Networks)

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摘要(中)

近年來大量的攝影機被架設於市區路口以協助檢視各種交通狀況，若能善用這些畫面將有助於先進智慧型運輸系統(Intelligent Transportation System)的建置。本研究嘗試以開放式政府資料庫蒐集市區道路監控影像，提出場景適應式行駛車輛偵測機制。由於路口攝影機通常有著不同角度的畫面，而畫面中可能存在各式背景，例如建築物、路邊物、招牌與行道樹等，加上人與車輛在道路上可能發生相互遮蔽的情況，都讓單一離線偵測模型存在若干改進空間。本研究所提出的方法分為兩個階段；第一階段蒐集少量市區道路影像，利用Faster R-CNN訓練通用車輛偵測模型，並對目標場景進行車輛偵測與分類。第二階段則利用背景建立法產生車輛遮罩，搭配第一階段的通用模型偵測結果，經比對蒐集足量的單一種類車輛，並以時序方式貼在目標場景中，以幾乎自動的方式產生大量該場景標記資料。我們將這些標記資料再以Faster R-CNN訓練第二階段場景適應式模型，以此模型進行車輛偵測及後續可能的車流估計。實驗結果顯示所提出的方法能有效偵測與分類市區場景車輛，對於遮蔽車輛偵測也有不錯的表現。

摘要(英)

A large number of digital cameras have been installed at intersections in urban areas to help monitor traffic conditions. Making better use of the scenes captured by these traffic surveillance cameras can facilitate the construction of advanced Intelligent Transportation Systems. This research aims at developing an adaptive vehicle detection scheme for urban traffic scenes, which collects roadside surveillance videos from publicly available sources. The proposed scheme consists of two main phases; the first phase is to collect a small number of traffic surveillance images for training a general model using Faster R-CNN. The second phase utilizes background subtraction to extract vehicle proposals. A sufficient number of vehicles are collected by comparing proposals with the results using the general model. The collected vehicles are superimposed on the constructed background in an appropriate order to achieve semi-automatic generation of training data with annotations. These training data are used to train a second-phase adaptive model. The experimental results show that the proposed scheme performs quite well and can handle vehicle occlusion problem.

關鍵字(中)

★ 市區道路影像
★ 自適應模型
★ 車輛偵測
★ 車輛識別
★ Faster R-CNN
★ 背景建立

關鍵字(英)

★ Urban Scenes
★ Adaptation Model
★ Vehicle Detection
★ Vehicle Recognition
★ Faster R-CNN
★ Background Subtraction

論文目次

論文摘要 i
Abstract ii
致謝 iii
目錄 iv
附圖目錄 vii
附表目錄 ix
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究貢獻 3
1.3 論文架構 4
第二章相關研究 5
2.1 傳統車輛偵測 5
2.1.1 前景背景分割方法 5
2.1.2 驗證車輛 7
2.2 物件偵測 8
2.2.1 R-CNN 10
2.2.2 Fast R-CNN 11
2.2.3 Faster R-CNN 12
第三章車輛偵測機制 14
3.1 系統概述 14
3.2 訓練通用模型 15
3.2.1 市區道路影像蒐集 16
3.2.2 標記資料介紹 17
3.2.3 Faster R-CNN訓練 18
3.3 物件提取 19
3.3.1 背景建立 20
3.3.2 遮罩分析 22
3.4 蒐集單一車輛 23
3.5 產生訓練資料 26
3.5.1 遮蔽車輛 28
3.5.2 多台車輛的順序 31
3.6 訓練自適應模型 34
第四章實驗結果 35
4.1 開發環境與人工資料集介紹 35
4.2 訓練樣本蒐集 37
4.3 通用模型與自適應模型比較 42
4.4 效果評測mAP 44
4.4.1 名詞介紹 44
4.4.2 mAP 45
4.4.3 評測結果 47
4.4.4 額外比較結果 50
第五章結論與未來展望 53
5.1 結論 53
5.2 未來展望 54
參考文獻 55

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指導教授

蘇柏齊(Po-Chyi Su)

審核日期

2018-8-17

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