臺灣博碩士論文加值系統

隨著世界各個國家之間的交流增加，對於彼此的交通需求也相應的上升，而臺灣四周被大海所環繞，對外的交通聯繫工具主要依賴船舶或者飛機，因此航運及航空產業長久以來一直都是臺灣的重點發展項目，其中，由於船舶的用途眾多且管理上較為分散，所以如何有效地進行水上活動管理一直是相關產業人員所關心的課題。目前國際海事組織基於航行安全上的考量，在海上人命安全規定中明文規範總噸位超過300噸的船舶需要強制裝設自動識別系統 (Automatic Identification System; AIS)；船舶可以依靠AIS送出自身的訊息，自己也能透過AIS獲取其他船隻的訊息，但對於沒有強制配備AIS的中小型船隻而言，管理上就顯得相對較為困難，需要花費較多的人力進行管控。近年來，隨著深度學習的快速發展，電腦視覺的技術被廣泛運用在多項任務當中，對於中小型船隻的管理，我們可以透過影像處理的方式分析岸邊監視器影像，以智慧化的方法協助管理水域交通狀況，以此減輕水域值班人員的負擔。
綜合上述內容，本論文提出一個透過岸邊監視器影像進行船隻追蹤與計數的方法。首先，我們通過Transformer對當前幀的影像進行偵測，獲得畫面中船隻的偵測框和追蹤框，接著，我們以模型的預測結果進行三個階段的匹配：第一階段會依照船隻外觀資訊進行匹配；第二階段會從被過濾的低分偵測框找回可能受到外部環境影響，而被過濾的船隻進行匹配；第三階段會根據卡爾曼濾波器提供的運動訊息進行匹配。於實驗中，我們比較其它三個最先進的多目標追蹤模型的性能，稱為FairMOT，ByteTrack，以及TransTrack，根據實驗結果顯示，我們的模型表現最好，其中精確率可以達到92.4%、召回率達到68.1%、多目標追蹤精度(MOTA) 達到0.625，而IDF1達到75.1%，並且成功找回資料集中72.5%的船隻軌跡。

With the increase in communication between various countries worldwide, the demand on transportation for each other has also increased accordingly. Taiwan is surrounded by the sea, and the primary means of transportation to foreign countries is by vessels or airplanes, so the shipping and aviation industry has long been a crucial development project for Taiwan. Among them, the effective maintenance of water traffic has always been the concern of the related personnel in the industry because of the many usages of vessels and comparatively decentralized management.
Currently, the International Maritime Organization (IMO) explicitly regulates the mandatory installation of an Automatic Identification System (AIS) for vessels over 300 gross tonnages for life safety at sea considerations. Vessels can rely on AIS to send their information and get information from other vessels through AIS. However, small vessels not mandatorily equipped with AIS are relatively difficult to manage and require more human resources for monitoring and control. In recent years, with the rapid development of deep learning, computer vision techniques have been widely used in many tasks. For the management of small vessels, we can analyze the shore-side surveillance video by the method of image processing to help manage the water traffic conditions in an intelligent way, thereby reducing the burden on the waterside duty personnel.
In this thesis, we propose an approach to tracking and counting vessels by shore-side surveillance video. Firstly, we detect the current frame of the video through Transformer to get the detection box and tracking box of the vessel in the frame. Then we perform three stages of matching with the model prediction results. The first stage will be matched according to the vessel's appearance information. The second stage will retrieve the vessels that may be affected by the external environment from the filtered low score detection frame for matching. And the third stage will be matched based on the motion information provided by the Kalman filter. In the experiment, we compare the performance of our model with that of three other state-of-the-art multiple object tracking models, namely FairMOT, ByteTrack, and TransTrack. According to the experimental results, our model performs the best, in which the precision, recall, multi-object tracking accuracy (MOTA), and the IDF1 can reach 92.4%, 68.1%, and 0.625, 75.1%, respectively. In addition, our model can successfully retrieve 72.5% of vessel trajectories in the dataset.

Contents
中文摘要 ii
Abstract iii
中文致謝 v
Contents vi
List of Figures viii
List of Tables x
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Motivation 2
1.3 System Descriptions 3
1.4 Thesis Organization 4
Chapter 2 Related Work 5
2.1 Multiple Object Tracking 5
2.1.1 Tracking by detection 6
2.1.2 Joint detection and tracking 6
2.2 Visual Transformer 8
Chapter 3 Vessel Tracking Method 13
3.1 Vessel Detection 13
3.2 Object Propagation 16
3.3 Data Association Enhancement 17
3.4 Loss Function 23
Chapter 4 Experimental Results and Discussion 24
4.1 Experimental Setup 24
4.2 Vessel Dataset and MOT Evaluation Metrics 25
4.3 Results of Vessel Tracking 30
4.4 Results of Vessel Counting 35
Chapter 5 Conclusions and Future Work 39
5.1 Contributions and Conclusions 39
5.2 Future Work 40
References 42

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