臺灣博碩士論文加值系統

監控系統已安裝於很多公共場所與自家住宅內來預防犯罪事件的發生與特定 安全事件的可疑人士的跟蹤。雖然在跨鏡頭的人物再辨識技術已經有很大的進展, 但在實際上多鏡頭的監視系統使用上，由於大量人員的進出導致與時間空間的不 確定性，以至於人員搜尋的時候仍是一項具有挑戰的任務。因此，本研究設計並 實踐了一監控系統，幫助用戶可跨多攝像頭連續穩定地追蹤同一個目標人物。首 先系統使用YOLO和Correlation filter來進行單鏡頭頭中的可疑人物追蹤，目標 人物的特徵也在此階段收集。一旦目標人員離開攝像機的範圍，則會藉由地理信 息和跟踪路徑來使用攝像機網絡拓撲結構預測候選入口攝像機。在第二階段中， 系統使用人員再辨識演算法計算人員相似性，並有助於在相機之間自動識別目標 人物。在實驗上，我們使用公開數據集進行測試，結果表明我們的系統可以準確 地在跨鏡頭識別目標人物並具有良好的表現。

Many public places, such as police agencies and universities, imported multiple cameras surveillance systems to prevent criminal events by tracking the suspicious. In the traditional surveillance system, if we need to identify the person in multiple cameras, it is necessary to interactively compare the characteristics of the target person within the Closed-Circuit Television(CCTV), which takes a lot of time. Despite the progress of intelligent surveillance systems, it has not become widespread products because the target person might pass by other persons, and occlusion causes the system to lose track of the target. What’s more, the recognition of the target person on multiple cameras remains a challenge because of the large spatio-temporal uncertainty, which means the entry and the exit of persons in cameras are unpredictable. Therefore, this paper proposes a novel system framework that achieves continuous and stable track of the same person in multiple cameras. Firstly, we utilize YOLO and Correlation filter to track the suspicious in single camera. Once the target enters another camera, the method we developed, which combines camera network topo-logy and Person re-identification technology, is able to identify the person automatically. Experimental results and public data-sets show our system can effectively solve the problems of single camera tracking, and accurately recognize the target across cameras. When retrieving the video record, the complete trajectory and appearance time are recorded, which improves the efficiency of the person searching and is more adapted in the real-world situation.

摘 要 1
Abstract 2
誌 謝 3
目錄 4
圖目錄 6
表目錄 7
第一章 緒論 8
1.1 研究動機與背景 8
1.2 研究目的與方法 9
1.3 章節概要 11
第二章 文獻探討 12
2.1 單鏡頭內的目標追蹤 12
2.1.1 Minimum Output Sum of Squared Error (MOSSE) 12
2.1.2 Discriminative Scale Space Tracker (DSST)13
2.1.3 Tracking-by-detection 15
2.2 跨鏡頭的目標追蹤 16
2.3 相機網路拓樸 18
第三章 研究方法 19
3.1 Tracking module 20
3.1.1 Detector 20
3.1.2 Tracker 20
3.1.3 Tracking by detection 21
3.1.4 Tracking Module 運作流程 22
3.2 Candidate Cameras Selection Module 23
3.2.1 In Camera network topology inference 23
3.2.2 Cross cameras topology inference 24
3.3 Person re-id Module 25
3.4 Recommend camera and result 27
3.5 Algorithm 29
第四章 實驗結果 33
4.1 Datasets 34
4.2 Tracking Experiment results 34
4.3 Person re-id experiment result 36
4.4 Experiments for Multiple Object Tracking Benchmark 39
4.5 Experiments for Person re-identification benchmark 40
第五章 Discussion and implementation 42
5.1 Discussion 42
5.2 Implementation 43
第六章 結論與未來展望 44
參考文獻 45

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