整合動態與靜態視覺技術的盲點區域車輛偵測

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姓名

馬紹宗(Shao-zong Ma) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

整合動態與靜態視覺技術的盲點區域車輛偵測
(Blind-spot Vehicle Detection with Dynamic and StaticVision Methods)

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★ 建立在動態載入多重解析度地形模塑的戰術模擬	★ 以多階分割的空間關係做人臉偵測與特徵擷取
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摘要(中)

近年來為了減少交通事故，許多車廠皆發展車輛輔助安全駕駛系
統，期望在許多危險發生前，預先給予駕駛警示。駕駛人在駕駛車輛時，在車的兩側都有一塊無法透過後視鏡觀察到的盲點視線範圍，若駕駛人沒有察覺盲點區域有其他車輛而進行變換車道，則有可造成碰撞。為了確保駕駛人在變換車道時盲點區域確實沒有其他車輛，我們利用在後照鏡下方架設相來拍攝盲點區域的影像，逶過電腦視覺的方法來偵測是否後方有可能造成威脅的來車。
我們提出的盲點偵測系統包含：定義盲點偵測範圍、在偵測範圍中
估計光流、將這些得到的光流做進一步篩選和群聚，得到這些由光流群聚所得到的可能為來車的移動物之後，最後在利用追蹤與穩定方法來做最後確認，得到的結果便很有可能是後方來車。另一方面，為了避免被追蹤車輛在追蹤過程中可能因為等速而造成光流向量消失而無法繼續以光流來偵測車輛，在此情況下我們以車底陰影來持續追蹤車輛。
我們在各種不同的道路環境下進行偵測，由實驗結顯示，在白天狀
況下市區的側邊車輛偵測率約為95%、在郊區的偵測率約為97%，而在夜晚狀況下偵測率約為90%。我們提出的盲點偵測法在Intel Core 2 Duo?E8400 3.0 GHz CPU, 2GB DDR RAM, Microsoft? Windows 7 機器上有著每秒30 張的處理速度。

摘要(英)

Developing a real-time automotive driver assistant system for safety has emerged wide attention in recent years. When driving on the road, the fields of view beside the host vehicle for drivers are limited. If the driver changes lane without being aware of the objects in the blind-spot area, the potential collision accident may occur. For ensuring the safety of changing lane, our method uses a camera mounted in side-view mirror to capture the image in blind-spot area and detects the vehicle with computer-vision technology.
The proposed method offers the blind-spot detection includes defining the detection and decision zone, estimating optical flow, filtering and grouping these estimated optical flow and using the process of tracking and stabilization to accomplish the detection. Considering the situation that optical flow disappears in consecutive tracking process, the proposed method detects the vehicle shadow to keep detecting and tracking. The proposed method also uses the shadow to enhance the detection result generated by optical flow.
We apply the proposed detection method to many different situations. In experiments, the detection rate in urban area in daylight is about 95%. The detection rate in suburban area is about 97%. The detection rate in night is
about 90%. The detection method operates in Intel? Core 2 Duo? E8400 3.0 GHz CPU, 2GB DDR RAM, Microsoft? Windows 7 has at least 30 frames per seconds.

關鍵字(中)

★ 盲點偵測
★ 光流
★ 智慧型車輛

關鍵字(英)

★ ITS
★ blind-spot detection
★ optical flow

論文目次

摘要 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ II
誌謝 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ III
目錄 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ IV
第一章緒論 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 一
第二章相關研究 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 二
第三章光流估計 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 三
第四章車輛偵測 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 四
第五章實驗 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 五
第六章結論及未來工作 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 六
附錄英文版論文 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 七
Abstract ............................................................................................................. ii
Contents ............................................................................................................ iii
Chapter 1 Introduction ...................................................................................... 1
1.1 Motivation ........................................................................................ 1
1.2 System overview .............................................................................. 2
1.3 Thesis organization ........................................................................... 3
Chapter 2 Related Works ................................................................................... 5
2.1 Feature-based detection methods ...................................................... 5
2.1 Optical flow-based detection method ............................................. 10
2.1 Sensor-based detection methods ..................................................... 18
Chapter 3 Optical Flow Estimation ................................................................. 20
3.1 Definition of optical flow and image flow ...................................... 20
3.2 Optical-flow estimation................................................................... 21
3.2.1 Horn and Schunck approach ..................................................... 21
3.2.2 Lucas and Kanade approach ..................................................... 25
3.2.1 Pyramidal structure approach ................................................... 27
Chapter 4 Vehicle Detection ............................................................................ 30
4.1 Definition of detection and decision region .................................... 30
4.2 Feature extraction ............................................................................ 33
4.3 Preprocessing of optical flow .......................................................... 34
4.3.1 Filtering optical flow ................................................................ 34
4.3.2 Grouping optical flow .............................................................. 36
4.4 Lateral vehicle detection ................................................................. 40
4.4.1 Lateral objects hypothesis ........................................................ 42
4.4.2 Lateral objects hypothesis verification ..................................... 43
4.5 Static-information extraction .......................................................... 48
Chapter 5 Experiments .................................................................................... 51
5.1 Experimental environments ............................................................ 51
5.2 Experimental results ........................................................................ 53
5.3 Discussion ....................................................................................... 58
Chapter 6 Conclusion and Future Works ........................................................ 59
6.1 Conclusions ..................................................................................... 59
6.2 Future works.................................................................................... 60

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指導教授

曾定章(Din-chang Tseng)

審核日期

2010-7-28

推文