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研究生: 朱永青
Yung-Ching Chu
論文名稱: 以軟硬體協同設計之混合型即時影像多目標物體追蹤系統
Hardware/Software Co-design of a Hybrid Multiple-Object Tracking System Based on Particle Filter and Particle Swarm Optimization
指導教授: 許陳鑑
Hsu, Chen-Chien
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 中文
論文頁數: 75
中文關鍵詞: 多目標物體追蹤可程式規劃系統晶片粒子群聚最佳化法粒子濾波器
英文關鍵詞: Multiple-object tracking, System on a Programmable Chip (SOPC), Particle Swarm Optimization, Particle Filter
論文種類: 學術論文
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  • 本文提出了一種混合型即時影像多目標物體追蹤系統,結合粒子群聚最佳化法(Particle Swarm Optimization, PSO)與粒子濾波器(Particle Filter, PF)兩種演算法之優點,以提升多目標物體追蹤的性能。並且在可程式規劃系統晶片(System On a Programmable Chip, SOPC)之架構下,利用FPGA(Field Programmable Gate Array)的硬體電路,將此混合型演算法以軟硬體協同設計(HW/SW Co-design)之方式實現出來。此方式不僅可以達到硬體加速的功能,還能有彈性地設計整個電路,當軟體部分驗證完成後,即可以全硬體方式實現整個多目標物體追蹤系統,進一步提升整體系統的效能。

    This thesis presents a hybrid algorithm incorporating Particle Swarm Optimization (PSO) and Particle Filter (PF) for multiple-object tracking to improve the system performance. Based on the System on a Programmable Chip (SOPC) technique, we use hardware/software (HW/SW) co-design method to implement the hybrid algorithm on the FPGA circuit. As a result, the tracking efficiency can be greatly improved, while maintaining design flexibility for various applications. To further improve the performance of the multiple-object tracking system, full hardware implementation of the tracking system can be realized once the prototype testing of the system is completed.

    表目錄 vi 圖目錄 vii 第一章 緒論 1 1.1研究背景與動機 1 1.2 論文架構 3 第二章 混合型多目標物體追蹤演算法 5 2.1 粒子群聚最佳化法 5 2.2 粒子濾波器 8 2.3 競爭選取法 11 2.4 混合型多目標追蹤演算法 12 第三章 軟硬體協同設計方法 14 3.1 多主從系統架構 14 3.2 Master端與Slave端設計介紹 16 3.2.1 Slave端設計 16 3.2.2 Master端設計 19 第四章 軟硬體協同設計平台 24 4.1 DE2-70多媒體開發平台 24 4.2 D5M擷取模組 27 4.2.1 D5M擷取模組腳位說明 29 4.2.2 D5M影像擷取格式 29 4.2.3 D5M擷取模組訊號時序圖 32 4.3 LTM觸碰面板模組 33 4.3.1 LTM顯示模組腳位說明 35 4.3.2 LTM顯示模組訊號時序圖 37 第五章 軟硬體協同設計之混合型多目標追蹤演算法 39 5.1 混合型多目標追蹤之硬體加速器 39 5.1.1 初始化模組 41 5.1.2 PSO粒子更新模組 43 5.1.3 PF粒子更新模組 46 5.1.4 權重計算模組 48 5.2 其他應用模組 52 5.2.1 隨機亂數模組 52 5.2.2 RAM 53 5.2.3 FIFO暫存器 54 5.2.4 狀態控制器 55 第六章 混合型演算法於多目標物體追蹤之硬體架構 57 6.1 CMOS影像擷取 59 6.2 影像追蹤 60 6.3 LTM影像顯示 61 第七章 實驗結果 62 7.1 軟體設計之多目標物體追蹤模擬結果 62 7.1.1 PSO與PF搜尋速度 63 7.1.2 PSO與PF追蹤性能 64 7.2 軟硬體協同設計之混合型多目標物體追蹤實驗結果 66 7.3 實際應用於多目標物體追蹤之實驗結果 68 第八章 結論與未來研究方向 71 8.1 結論 71 8.2 未來展望 72 參考文獻 73

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