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研究生:吳侒融
研究生(外文):An-Rong Wu
論文名稱:卷積神經網路與自我關注機制於車禍偵測之應用
論文名稱(外文):Traffic Accident Detection Using Convolutional Neural Network and Self-attention Mechanism
指導教授:方文賢陳郁堂
指導教授(外文):Wen-Hsien FangYie-Tarng Chen
口試委員:鍾聖倫賴坤財丘建青方文賢陳郁堂
口試委員(外文):Sheng-Luen ChungKuen-Tsair LayChien-Ching ChiuWen-Hsien FangYie-Tarng Chen
口試日期:2020-07-30
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:69
中文關鍵詞:車禍偵測行車記錄器之車禍數據集卷積神經網路自我關注機制自動駕駛
外文關鍵詞:Accident detectionDashcam accident datasetCNNSelf-attentionAutonomous vehicles
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擁有辨識道路上異常情況能力的交通事故偵測,由於先進駕駛輔助系統(ADAS)、視頻監控、交通分析等廣泛的應用而越來越受到重視。本論文提出了一種新型的交通事故偵測架構用於行車記錄器視頻,該方法首先利用時間關係網路來處理一系列視頻幀以生成時空特徵,之後,採用雙向自我關注機制來有效地學習跨幀間的長期時間依賴性。此外,為了便於架構的培訓,我們也從YouTube、行車記錄器互助網(VEDR.tw)和Facebook中蒐集以臺灣地區為主的車禍事故影片,並提供時間標記以提升交通事故檢測的性能。我們將新數據集稱為ITRI數據集,其中包含了如雨天,明亮度,晝夜變化,隧道場景等更具有挑戰性的問題。最後,我們在常用的DAD和ITRI數據集上實現我們的方法,透過雙向自我關注機制的調用,該網路架構提供了比先前的方法更高的性能。
Traffic accident detection, which needs to recognize the abnormal movements on the road, is getting more attention due to a wide range of applications, such as Advanced Driver Assistance Systems (ADAS), video surveillance, and traffic analysis. This thesis proposes a novel architecture for traffic accident detection in dashcam videos. The new method first utilizes a temporal relation network to process a sequence of frames to generate spatio-temporal features. Afterward, a bidirectional self-attention mechanism is employed to effectively learn the long-term temporal dependency across the frames. Furthermore, to facilitate the training of the method, we also collect a large number of car accident videos appearing in Taiwan from YouTube, VEDR.tw, and Facebook, and provide the temporal-annotations to boost the performance of traffic accident detection. This dataset, referred to as the ITRI dataset, has a variety of challenging issues, such as raining, illuminations, day-night situation, tunnels, etc. Finally, we implement our method on the commonly used DAD and ITRI datasets. By invoking the bidirectional self-attention mechanism, the network provided superior performance compare with previous works.
摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Table of contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 Accident Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 CNN Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4 Attention Network . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1 Overall Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Spatial Feature Generation . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Transformer Encoder . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.1
Scaled Dot-Product Attention . . . . . . . . . . . . . . . . 14
3.3.2
Multi-Head Attention . . . . . . . . . . . . . . . . . . . . . 15
3.4 Bidirectional Self-Attention . . . . . . . . . . . . . . . . . . . . . 16
3.5 Multi-Scale Temporal Relations . . . . . . . . . . . . . . . . . . . 18
3.6 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Experimental Results and Discussion . . . . . . . . . . . . . . . . . . . 20
4.1
Accident Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.1.1 Video Collection . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.2 Data Annotation . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.3 Dataset Distribution . . . . . . . . . . . . . . . . . . . . . 27
4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.4.1 Impact of Different Window Length . . . . . . . . . . . . . 30
4.4.2 Impact of the Different Segment . . . . . . . . . . . . . . . 32
4.4.3 Impact of Bidirectional Self-Attention . . . . . . . . . . . . 33
4.5 Comparison with Previous Works . . . . . . . . . . . . . . . . . . 34
4.6 Successful Cases and Error Analysis . . . . . . . . . . . . . . . . . 36
4.6.1 ITRI dataset . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.6.2 DAD dataset . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.7
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5 Conclusion and Future Works . . . . . . . . . . . . . . . . . . . . . . . 48
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Appendix A : Example images from the DAD dataset . . . . . . . . . . . . 49
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
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