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| 研究生： |
邱建毓 Chiou, Chien-Yu |
|---|---|
| 論文名稱： |
使用稀疏表示法之異常駕駛行為偵測 Abnormal Driving Behavior Detection Using Sparse Representation |
| 指導教授： |
詹寶珠
Chung, Pau-Choo |
| 學位類別： |
碩士 Master |
| 系所名稱： |
電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering |
| 論文出版年： | 2016 |
| 畢業學年度： | 104 |
| 語文別： | 英文 |
| 論文頁數： | 55 |
| 中文關鍵詞： | 駕駛者監控系統 、稀疏重構 、正常模型 、異常偵測 |
| 外文關鍵詞： | Driver monitoring system, sparse reconstruction, normal model, anomaly detection |
| 相關次數： | 點閱：83 下載：1 |
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近年來，為減少交通事故發生的機率，已經有許多駕駛者監控系統被開發出來。當駕駛者出現異常駕駛狀況時，系統會向駕駛者發出提醒。然而，目前既有的系統與演算法，需要事先列舉欲偵測的異常駕駛狀況，因此難以運用在實際道路駕駛上。本篇論文提出一套為駕駛者建立正常駕駛狀態模型的全新駕駛者監控系統。我們提出的系統可以藉由稀疏表示法與重構，來比較駕駛者當前的駕駛狀態與其個人化正常駕駛模型之間的差異。當駕駛發生明顯外觀異常時，我們的系統毋須經過事前定義，便可以偵測出駕駛者處於異常駕駛狀況。為了能夠有效地描述影像中駕駛者當前的駕駛狀態，本論文同時也提出一種帶時間性的新式描述元來描述駕駛者臉部區域的影像與狀態。在實驗中中，我們驗證了系統的準確性。相較於過去的方法，我們提出的系統利用駕駛者的個人化正常駕駛模型，可以更有效地監控駕駛者的駕駛狀態，並偵測到不特定的異常駕駛狀況。
To reduce the chance of traffic crashes, many driver monitoring systems (DMS) have been developed. A DMS warns the driver when the driver is under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. This paper proposes a novel DMS, which models the driver’s normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver’s statuses with his personal normal driving status model and identify abnormal driving statuses that greatly change the driver’s appearance without predefining them. To appropriately represent the driver’s statuses in videos, this paper proposes a novel temporal descriptor for representing driver’s face regions. The experimental results show good performance of the proposed DMS.
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校內：2021-09-01公開校外：不公開