博碩士論文 103522083 詳細資訊

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姓名 吳俊賢(Jun-Xian Wu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 低複雜度的嵌入式物件偵測與追蹤系統設計
(Low-Complexity Embedded Object Detection and Tracking System Design)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 (2022-1-17以後開放)
摘要(中) 物聯網概念的興起,低複雜度的感測節點成為開發的趨勢。本論文為將完整的物件偵測和追蹤實作於低成本嵌入式DSP平台,並進行效能上的驗證,目的是開發出一個低複雜度、低硬體資源需求,且無作業系統的智慧型嵌入式攝影機系統。在移動物偵測方面,我們採用近似中值濾波法實作背景建模,由於其複雜度低,不需太多的記憶體資源,因此作為物件偵測的主要方法。接著我們應用PSO演算法,偵對移動物建立移動物樣板,並利用粒子群全域最佳化進行非線性的移動物追蹤。此系統在記憶體資源有限及開發成本有限的情況下,能達到即時運算的需求,且系統以C語言作為開發工具,可移植性高,未來可作為物聯網中智慧監控攝影機的應用。
摘要(英) In rising of IoT, low-complexity sensor node is the trend of development. This paper implements complete object detection and tracking on a low-cost DSP platform, and verify the system performance on efficacy. Our goal is to achieve an intelligent embedded camera of low-complexity, hardware-constrained, and without operating system. For detection, because of the low-complexity, the paper utilize Approximated Median Filter (AMF) to achieve background modeling for the main method of object detection. Then particle swarm optimization (PSO) is main method which is used as tracking strategy: First, build the target model for moving object. Through PSO algorithm, the system can track moving objects in the nonlinear system. Limited on the memory and development costs, the experiments and analysis still show the efficiency. Due to using C language as development tools, the system is high portability. The proposed system can be the IoT application system case in the future.
關鍵字(中) ★ 物件偵測
★ 物件追蹤
★ 低複雜度
★ 嵌入式
關鍵字(英) ★ Object Detection
★ Object Tracking
★ Low-Complexity
★ Embedded
論文目次 摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
第一章、緒論 1
1.1研究背景 1
1.2研究目的 2
1.3論文架構 2
第二章、文獻回顧 3
2.1背景移除法(Background Subtraction) 3
2.1.1影像差異法(Frame Difference) 4
2.1.2近似中值濾波法(Approximated Median Filtering) 4
2.1.3單高斯背景模型(Single Gaussian Background Model) 5
2.1.4混和高斯背景模型(Gaussian Mixture Model, GMM) 6
2.1.5碼本背景模型(Codebook Background Model,CB) 8
2.2物件追蹤 8
2.2.1連通區塊追蹤(Blob Tracking) 9
2.2.2輪廓追蹤(Contour Tracking) 9
2.2.3特徵追蹤(Visual Feature Matching) 9
2.2.4多特徵聯合稀疏表示(Multi-Feature Joint Sparse Representation) 10
2.2.5粒子濾波器追蹤(Particle Filter Tracking) 10
2.2.6粒子群體最佳化(Particl Swarm Optimization Tracking, PSO) 10
2.3嵌入式視覺(Embedded Vision) 12
第三章、嵌入式物件偵測與追蹤系統設計 14
3.1 MIAT系統設計方法論 14
3.1.2 GRAFCET離散事件建模 17
3.2系統架構設計 18
3.3離散事件建模 20
3.3.1物件偵測模組 21
3.3.2特徵擷取 24
3.3.3物件追蹤模組 25
3.4軟體合成 27
第四章、DSP系統實作與整合驗證 31
4.1實驗平台 31
4.2 DSP嵌入式視覺系統開發流程 35
4.3嵌入式軟體實作 37
4.4系統整合驗證與實驗 41
4.4.1系統整合驗證 41
4.4.2效能分析 43
第五章、結論與未來研究方向 45
5.1結論 45
5.2未來研究方向 46
參考文獻 47

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指導教授 陳慶瀚(Ching-Han Chen) 審核日期 2017-1-18
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