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.

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