AIoT攝影機網路閘道器設計與實作

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姓名

邱耀德(Yao-Te Chiu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

AIoT攝影機網路閘道器設計與實作
(Design and Implementation of AIoT Camera Network Gateway)

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[完整記錄]

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至系統瀏覽論文 (2027-7-4以後開放)

摘要(中)

佈署基於深度學習物件偵測和追蹤應用，通常需要仰賴雲端的大型主機。隨著邊緣攝影機的數量越來越多，基於雲端運算的 AI 系統，有著頻寬消耗大、延遲長、以及功耗高等缺點，因此導致近年來邊緣運算快技術速崛起。本研究提出一個創新的 AIoT 平台，可用於佈署 AI-enabled 攝影機網路。我們將物件追蹤部署至邊緣端，藉此降低系統的時間延遲。本系統藉由彈性擴增運算單元，針對不同邊緣攝影機數量，快速調整閘道器的運算單元，以便達成藉此達成效能需求和功耗的平衡。透過攝影機網路的協同追蹤，物件在昏暗環境或是物體微小的情況，皆能夠成功偵測和追蹤。我們以行人追蹤作為實驗測試資料，得出我們的平台在網路傳輸效率上相較大型主機節省了 64%的時間，而 AI推論時間上則有 10%之效能提升，加上此一 AIoT 平台彈性架構和低功耗的優勢，非常適合應用於 AI 服務需求經常變動的環境。

摘要(英)

Deployment of deep learning-based object detection and tracking applications, which typically rely on cloud-based mainframes. With the increasing number of edge cameras, cloudbased AI systems have the disadvantages of high bandwidth consumption, high latency, and high power consumption, resulting in the rapid rise of edge computing technology in recent years. This study proposes an innovative AIoT platform that can be used to deploy AI-enabled camera networks. We deploy object tracking task to the edge to reduce the latency of the system. The system is designed to achieve a balance between performance requirements and power consumption by flexibly scaling up the computing units and quickly adjusting the gateway′s computing units for different numbers of edge cameras. With the collaborative tracking of the camera network, objects can be successfully detected even if they are small or in low light environments. Using pedestrian tracking as our experimental test data, we found that our platform saves 64% of time in network transmission efficiency compared to mainframes, and has a 10% performance improvement in AI inference time. Coupling with the flexible architecture and low power consumption of this AIoT platform, makes it ideal for applications in environments where the demand for AI services change frequently.

關鍵字(中)

★ 邊緣計算
★ 攝影機網路
★ 分散式運算

關鍵字(英)

★ Edge Computing
★ Camera Network
★ Distributed Computing

論文目次

摘要 I
Abstract II
誌謝 III
圖目錄 VII
表目錄 IX
第一章、緒論 1
1.1 研究背景 1
1.2 研究目標 3
1.3 論文架構 3
第二章、文獻回顧 4
2.1 物件追蹤 4
2.1.1 光流法 4
2.1.2 Deep SORT 6
2.2 多攝影機偕同運作 8
2.2.1 重疊視野攝影機 8
2.2.2 非重疊視野攝影機 9
2.2.3 攝影機連結模型 10
2.3 AIoT系統架構 13
第三章、 AIoT攝影機網路系統設計 18
3.1 攝影機網路 18
3.1.1 物件追蹤與重識別 19
3.1.2 單攝影機追蹤 20
3.1.3 多攝影機物件追蹤 21
3.2 AIoT閘道器平台設計 23
3.2.1 虛擬化與容器化技術 24
3.2.2 無線傳輸與gRPC 26
3.3 AIoT攝影機網路階層式系統設計 28
3.3.1 邊緣攝影機模組 29
3.3.2 AIoT攝影機系統模組 30
3.3.3 網路傳輸模組 30
3.3.4 AIoT攝影機系統中控模組 31
3.3.5 網路負載平衡模組 32
3.3.6 攝影機網路追蹤模組 33
3.4 AIoT攝影機網路系統離散事件建模 33
3.4.1 邊緣攝影機模組離散事件建模 34
3.4.2 網路傳輸模組離散事件建模 35
3.4.3 AIoT攝影機系統模組離散事件建模 36
3.4.4 AIoT攝影機系統中控模組離散事件建模 37
3.4.5 網路負載平衡模組離散事件建模 38
3.4.6 攝影機網路追蹤模組離散事件建模 39
第四章、 AIoT攝影機網路實驗 42
4.1 實驗環境 42
4.1.1 實驗資料集 42
4.1.2 軟硬體實驗環境 44
4.2 資源消耗實驗 46
4.2.1 功耗計算實驗 47
4.2.2 穩定度實驗 48
4.2.3 邊緣攝影機影像傳輸實驗 50
4.3 彈性擴增及網路傳輸壓力測試實驗 50
4.4 AIoT攝影機網路運行實驗 52
4.4.1 AIoT攝影機網路行人追蹤實驗架構 52
4.4.2 AIoT攝影機網路行人追蹤實驗結果 53
第五章、結論與未來展望 57
5.1 結論 57
5.2 未來展望 58
參考文獻 59

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2022-7-13

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