一個即時移動物偵測與追蹤的嵌入式系統

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顏妙純(Miao-chun Yen) 查詢紙本館藏

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論文名稱

一個即時移動物偵測與追蹤的嵌入式系統
(An Embedded System for Real-Time Detection and Tracking of Moving Object)

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摘要(中)

在視訊監控、機器人視覺的應用，物件偵測與追蹤扮演著重要的角色。本論文致力於提出一個強健且高效率的演算法，以便進一步實現即時動態物件偵測和追蹤的嵌入式系統。
在移動物偵測方面，我們採用漸進法建構背景，在變動的背景下，仍可適應性的更新的背景資訊，以便可靠地擷取出移動物件。接著我們應用PSO演算法進行移動物之追蹤。先建立移動物樣版，並利用粒子群的全域最佳化搜尋特性進行非線性、非固定數目的移動物追蹤。經由單人、多人、及有遮蔽情況的追蹤實驗，我們的方法在準確性與執行效率均較傳統方法為佳。
最後我們將此一演算法移植到嵌入式系統上，結合Pan/Tilt攝影機架設於移動式機器人之頭部進行即時的移動物偵測和追蹤。此一系統在極其有限的記憶體資源和低速的處理器的硬體資源下，以達到即時運算的性能需求。

摘要(英)

Visual tracking has been a popular application in computer vision, for example, public area surveillance, and robot vision, etc. This paper presents a robust and efficient algorithm for detecting and tracking moving objects, so that we can achieve an embedded system for real-time detection and tracking of moving objects.
For detection, we utilize a progressive and adaptive background generation. Even if the unstable noise, for example, light changed and fluttering leaves, we still extract the foreground objects exactly. Then particle swarm optimization (PSO) is used for tracking as a search strategy. First, build the target model, and through the PSO, we can track moving objects in the nonlinear system. Experiment shows that the proposed method can track the single person, multiple people even when occluded, and is more efficient and accurate than the traditional methods.
Eventually, we transplant the algorithm into the embedded system, and the CPU is ARM Cortex-M3. The COMS sensor is placed on a pan/tilt platform in order to track the moving object. Limited on the CPU and memory, the experiments and analysis still show the efficiency.

關鍵字(中)

★ 移動物偵測
★ 移動物追蹤
★ 嵌入式系統

關鍵字(英)

★ moving object tracking
★ embedded system
★ moving object detection

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章緒論 1
1.1研究動機 1
1.2文獻探討 2
1.2.1物件偵測 2
1.2.2物件追蹤 3
1.2.3機器人視覺追蹤應用 6
1.3系統架構 7
1.4論文架構 7
第二章物件偵測 8
2.1背景模型 8
2.2陰影去除 12
2.3影像前處理 14
2.3.1移動物件偵測 14
2.3.2型態學影像處理 15
2.4移動物分割 18
2.4.1連通元件 18
2.4.2等分區塊分割 19
第三章物件追蹤 23
3.1定義搜尋空間 24
3.2追蹤方法 25
3.2.1全域搜尋 25
3.2.2 PSO-based追蹤 26
3.2.3物件特徵 28
3.3新移動物出現 32
3.4遮蔽處理 34
第四章演算法評估與實驗 36
4.1追蹤演算法的實驗比較 36
4.1.1粒子濾波器介紹 36
4.1.2實驗結果 39
4.1.3結果分析 43
4.2移動物偵測實驗 44
4.2.1建立背景 44
4.2.2去除陰影 46
4.3移動物追蹤實驗結果 48
4.4遮蔽處理 52
第五章嵌入式系統實作 54
5.1嵌入式硬體 54
5.1.1 MIAT-STM32實驗版 54
5.1.2 ARM Cortex-M3嵌入式處理器 55
5.1.3影像感測器與即時取像 56
5.1.4 Pan/Tilt嵌入式視覺平台 57
5.2 嵌入式軟體開發 58
5.2.1嵌入式軟體開發平台 58
5.2.2即時影像處理(Real-Time Image Processing) 59
5.2.3系統整合架構 61
5.3 實驗結果與分析 62
5.3.1實驗結果 62
5.3.2系統的記憶體資源使用 63
5.3.3效能分析 63
第六章結論與未來方向 65
6.1 結論 65
6.2 未來方向 65
參考文獻 67

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指導教授

陳慶瀚(Ching-han Chen)

審核日期

2009-7-7

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