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研究生(外文):Hsin-Lun Wu
論文名稱(外文):Stop-and-go Detection using Dynamic and Static Vision Clues
指導教授(外文):Din-Chang Tseng
外文關鍵詞:corner detectionstop-and-gooptical flow
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前車停止與啟動偵測的方法在Intel Pentium Core2 Duo 1.86GHz及2GB RAM的個人電腦上執行,可達每秒150至160張畫面,正確率可達95%。
Due to the concentration of population in cities, the traffic flow of the urban area is progressively growing and then more collision and accidents are raised. In this study, we design a safty detection which is focused on the following cases. When driving in cities, drivers will spend much time waiting for the transformation of the traffic signal or sticking in traffic jam. During the transformation of the traffic signal or sticking in traffic jam, if the front of the vehicle forward to leave or stop, the driver do not pay attention may cause inconvenience or collision. For the safety of drivers, the stop-and-go detection method is proposed in this study. In the stop-and-go detection method, corners are used as features to calculate optical flow. According to length and direction of the optical flow, we use different methods to filter optical flow in different regions and adjust the length of optical flow. After obtaining the dynamic information, integrating static information into dynamic information for clustering optical flows to get moving blocks. Finally, we use these moving blocks by the tracking skill to judge whether the front vehicle is stopping or going. This detection method can also avoid the effects of vehicles in different direction, variant weather, and the light at nighttime.
The proposed methods are evaluated in several variant environments. The detection rate of stop-and-go method is 95% and the frame rate is 150 to 160 frames per second.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 x
第一章 緒論 1
1.1 研究動機 1
1.2 系統架構 2
1.3 論文架構 5
第二章 相關研究 6
2.1 前車停止與啟動偵測 6
2.2 障礙物偵測 9
2.3 角點偵測 19
第三章 特徵擷取與光流向量估計 24
3.1 角點偵測 24
3.2 光流向量估計 25
第四章 前車停止與啟動偵測 27
4.1 偵測區域設定 27
4.2 光流向量篩選與調整 29
4.2.1 光流向量篩選 30
4.2.2 光流向量調整 34
4.3 光流向量結合色彩資訊的分群 38
4.3.1 光流向量分類 38
4.3.2 定義光流向量的顏色資料結構 40
4.3.3 濾除地面標誌光流 43
4.3.4 結合色彩資訊的光流分群 43
4.4 多重群聚區塊中的重疊區塊處理 47
4.5 時間序列中各區塊的一致性分析 49
第五章 實驗 51
5.1 實驗環境 51
5.2 篩選光流方法結果比較 51
5.3 分群方法結果比較 55
5.4 前車停止與啟動偵測結果 57
5.4.1 前車停止偵測 57
5.4.2 前車啟動偵測 64
5.5 實驗效能分析 70
第六章 結論與未來展望 72
6.1 結論 72
6.2 未來展望 73
參考文獻 74
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