博碩士論文 102522037 詳細資訊




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姓名 邱繼德(Chi-Te Chiou)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 運動物件追蹤硬體加速器設計與實作
(Design and Implementation of Hardware Accelerator for Motion Object Tracking)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2021-4-25以後開放)
摘要(中) 在視訊監控和機器人視覺應用,物件追蹤扮演了重要的角色。典型的物件追蹤演算法需要大量計算資源,導致在資源受限的嵌入式系統實現即時物件追蹤的困難。本論文致力於設計一個平行化架構的物件追蹤硬體加速器。此系統包含了特徵擷取模組、預估位置模組及PSO追蹤模組,以及最上層的管線控制器。特徵擷取模組利用灰度統計和哈爾特徵建立多特徵聯合稀疏矩陣,作為追蹤物的樣板資訊,接著預估搜尋範圍,最後粒子群最佳化追蹤物件移動。我們採用開放資料庫進行驗證和測試。實驗結果顯示,我們的系統能夠滿足即時追蹤的性能需求。同時與先前的研究結果比較,本系統減少了34%的硬體資源使用。
摘要(英) Object tracking has been a popular application in computer vision, for example,public area surveillance, and robot vision, etc. Due to typical object tracking algorithm needs high-efficiency hardware resources to reduce the processing time, it is difficult to implement a real-time object tracking in resource-constrained embedded systems. In this paper, we design a parallel architecture object tracking hardware accelerators. The architecture of the accelerator contains Feature module, Prediction module, PSO tracking module and a top layer pipeline controller. Feature module constructs multi-feature joint sparse matrix by using grayscale statistics and Haar-like features, and uses them as the template of the tracking object. Then, estimates the searching scale. Finally, Particle Swarm Optimization (PSO) is used for tracking object’s movement. We adopt open source database to verify and test our modules. Experimental result shows that our system can satisfy real-time object tracking requirement. Comparing with previous research consequence, our system reduces 34% usage of hardware resource.
關鍵字(中) ★ 硬體加速器
★ 物件追蹤
★ 設計與實作
關鍵字(英) ★ Hardware Accelerator
★ Object Tracking
★ Design and Implementation
論文目次 摘 要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 X
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 3
第二章、文獻回顧 4
2.1 多特徵聯合稀疏表示 4
2.2 定義搜尋空間 10
2.3 物件追蹤方法 11
2.3.1 卡爾曼濾波器(Kalman Filter) 12
2.3.2 粒子濾波器(Particle Filter) 15
2.3.3 PSO-based粒子群最佳化追蹤 17
2.4 物件追蹤硬體加速技術 21
第三章、運動物件偵測系統設計 26
3.1 系統架構設計 27
3.1.1 物件追蹤系統架構 30
3.2 離散事件建模 32
3.2.1 物件追蹤系統Grafcet建模 36
第四章、運動物件偵測硬體合成 48
4.1 物件追蹤系統硬體合成 49
4.2 特徵擷取模組 52
4.3 預估位置模組 53
4.4 PSO追蹤模組 55
4.4.1 初始化粒子位置與速度模組 57
4.4.2 更新粒子位置及速度並傳遞粒子模組 58
4.4.3 計算是否符合物件特徵模組 59
4.5 管線化控制器設計 60
4.6 PSO追蹤模組平行化設計 63
第五章、實驗結果與探討 66
5.1 實驗平台 66
5.2 驗證方法 68
5.3 系統驗證 68
5.3.1 多特徵聯合稀疏矩陣驗證 69
5.3.2 PSO追蹤驗證 70
5.3.3 物件追蹤系統驗證 71
5.4 實驗結果比較與分析 75
第六章、結論 77
6.1 結論 77
6.2 未來研究方向 78
參考文獻 79
參考文獻

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指導教授 陳慶瀚(Ching-Han Chen) 審核日期 2016-4-25
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