車輛與行人軌跡在許多交通分析應用上是相當重要的參考資訊。本研究針對繁雜的路口交通環境，透過深度學習自動偵測空拍影像中的人車位置，進而取出大量豐富的人車軌跡資訊。本研究的主要貢獻有三：(1)結合空拍機與深度學習、影像處理等多項跨領域技術，以實際的十字路口為測試對象，完成了一個高度自動化的人車軌跡抽取技術。(2)利用Mask R-CNN與YOLOv3等物件偵測方法，解決偵測框貼合車輛形狀以及行人等小物件偵測問題；(3)將傳統軌跡資訊從線拓展為面的行經區域資訊，進一步豐富軌跡資訊。未來透過自動提取出2D平面上的軌跡資訊，可提供交通分析上的有利參考。

Vehicle and pedestrian trajectories are important references for many traffic analysis applications. In this study, we focus on the complex traffic environment at intersections and use deep learning to automatically locate the positions of vehicles and pedestrians in aerial images, and then extract a large amount of rich information on vehicle and pedestrian trajectories. There are three main contributions in this study: (1) Combining aerial camera with several cross-domain technologies such as deep learning and image processing, a highly automated human/vehicle trajectory extraction technology is accomplished using real intersections as test data. (2) Solve the problem of fitting bounding boxes to vehicles and detecting small objects such as pedestrians by using Mask R-CNN and YOLOv3, respectively; (3) Expanding traditional trajectory information from lines to travel area information to further enrich trajectory information. By automatically extracting the trajectory information on 2D maps, it helps to enrich the information needed for advanced traffic analysis.