基於人工蜂群演算法之物件追蹤研究

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姓名

蔡尚麟(Shang-Lin Tsai) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於人工蜂群演算法之物件追蹤研究
(Objects Tracking Based on Artificial Bee Colony Algorithm)

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摘要(中)

近年來，隨著攝影機與監視器的普及，影像追蹤成為了一個熱門的議題。為了提升追蹤目標物的精準度和解決目標物遮蔽的問題，本論文採用人工蜂群(Artificial Bee Colony; ABC)演算法來對目標物進行即時追蹤。
在偵測目標物這方面，本論文採用了背景相減法，因其可以切割出完整的目標物體且運算量低，容易運用於即時系統中。再來則是利用改良的種子區域生長法來標記各個目標物，區分出各個目標物後，再計算出各個目標物的中心位置。接著對各個目標物建構顏色直方圖模型以便做追蹤使用，追蹤過程中很容易受到光線變化影響，本文是採用HSV色彩空間中的色相，去掉了亮度的影響可以成功降低光線變化所造成的干擾。物件追蹤則是利用了人工蜂群演算法來尋求最佳解，擁有結構簡單、容易使用及收斂速度快等特性。遮蔽物問題一直以來都是物件追蹤的一個問題，為了有效的解決遮蔽物問題，本論文使用了一個可以調整搜尋框大小的機制，在追蹤不到目標物的時候放大搜尋框來增加搜尋範圍，追蹤到目標物的時候又能將追蹤框調整回原來的大小。

摘要(英)

In recent years, as cameras and monitors become more and more popular, object tracking becomes a hot issue. In order to improve the accuracy of the tracking object and solve the occlusion problem, in this thesis, the Artificial Bee Colony (ABC) algorithm is used for object tracking in real time.
In terms of object detection, in this thesis, the background subtraction is used for it can cut out complete targets, has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target and calculate its center. Then, for model building, color histograms are used to build target models. In order to avoid the interference of light, in this thesis, the HSV (Hue, Saturation and Value) color space is used. Moreover, for object tracking, in this thesis, the ABC algorithm which has a simple structure is used to find the best solution for it is easily used and its convergence is fast. Occlusion is always a big problem for object tracking. Therefore, in this thesis, the adaptive searching window is applied to exclude occlusion; the searching window will zoom in or out, depending on its fitness value. If the tracking window loses the targets, the searching window will increase. If the tracking window finds the targets, the searching window will adjust to the original size.

關鍵字(中)

★ 人工蜂群演算法
★ 物件追蹤
★ 物件偵測
★ 種子區域生長法

關鍵字(英)

★ ABC
★ Object tracking
★ Object detection
★ Seeded region growing

論文目次

目錄
中文摘要 i
英文摘要 ii
目錄 iv
圖目錄 vii
表目錄 x
第一章緒論 1
1-1 簡介 1
1-2 研究動機與方法 1
1-3 文獻回顧 2
1-4 主要成果與貢獻 4
1-5 論文架構 4
第二章軟硬體與系統架構 5
2-1 外部硬體 5
2-2 內部軟體 6
2-3 系統架構 7
第三章偵測目標物與建模 8
3-1 色彩空間 9
3-1-1 RGB色彩空間 9
3-1-2 YUV色彩空間 10
3-1-3 HSV色彩空間 11
3-1-4 RGB轉灰階 12
3-1-5 RGB轉HSV 12
3-2 目標物體偵測 13
3-2-1 連續影像相減法（Temporal difference） 13
3-2-2 光流法（Optical flow） 13
3-2-3 背景相減法(Background substraction) 14
3-3 形態學處理 15
3-3-1 侵蝕(Erosion) 16
3-3-2 膨脹(Dilation) 17
3-3-3 斷開(Opening) 18
3-3-4 閉合(Closing) 19
3-4 影像分割 21
3-4-1 區域分裂與合併(Region Splitting and Merging) 21
3-4-2 種子區域生長法(Seeded Region Growing) 22
3-4-3 改良種子區域生長法 25
3-5 目標物建模 28
第四章追蹤方法與分析 31
4-1 人工蜂群演算法 32
4-1-1 人工蜂群演算法背景 32
4-1-2 蜜蜂的行為模式 32
4-1-3 人工蜂群的數學模式 35
4-1-4 人工蜂群之演算程序 37
4-1-5 控制參數的影響 40
4-2 解空間與搜尋空間 41
4-2-1 解空間 41
4-2-2 搜尋空間 41
4-3 適應函數 43
4-4 遮蔽問題探討 44
4-5 完整追蹤流程 49
第五章實驗結果與討論 51
5-1 模擬實驗 51
5-2 實際測試 55
第六章結論與建議 65
6-1 結論 65
6-2 建議 65
參考文獻 66

圖目錄
頁次
圖 2.1 Kinect感測器外觀 5
圖 2.2 C#操作介面 6
圖 2.3 追蹤演算法基本流程圖 7
圖 3.1 物件偵測基本流程圖 8
圖 3.2 RGB加法混色法 9
圖 3.3 RGB色彩模型 9
圖 3.4 YUV色差模型 10
圖 3.5 HSV色輪 11
圖 3.6 背景相減法實際測試圖 15
圖 3.7 二值化圖像 15
圖 3.8 四鄰域與八鄰域 16
圖 3.9 四鄰域侵蝕過程 16
圖 3.10 八鄰域侵蝕過程 16
圖 3.11 侵蝕一次實際測試 17
圖 3.12 四鄰域膨脹過程 17
圖 3.13 八鄰域膨脹過程 17
圖 3.14 膨脹一次實際測試 18
圖 3.15 四鄰域斷開過程 18
圖 3.16 斷開實驗測試 19
圖 3.17 四鄰域閉合過程 19
圖 3.18 閉合實際測試 20
圖 3.19 先侵蝕一次在膨脹兩次 20
圖 3.20 先侵蝕一次在膨脹三次 20
圖 3.21 區域分割與合併示意圖 21
圖 3.22 目標物分割假想圖 23
圖 3.23 種子區域生長法掃描一次 24
圖 3.24 種子區域生長法的生長過程示意圖 24
圖 3.25 改良區域種子生長法標記情形 26
圖 3.26 改良區域種子生長法掃描一次 27
圖 3.27 改良種子區域生長法示意圖 27
圖 3.28 建構目標物直方圖模型示意圖 29
圖 4.1 物件追蹤基本流程 31
圖 4.2蜜蜂的覓食行為 34
圖 4.3 ABC流程圖 39
圖 4.4 追蹤框大小示意圖 41
圖 4.5 物體移動示意圖 42
圖 4.6 慣性運動和非慣性運動示意圖 42
圖 4.7 搜尋框與追蹤框示意圖 45
圖 4.8 搜尋框與追蹤框示意圖 46
圖 4.9 大型遮蔽物示意圖 47
圖 4.10 自適應搜尋框示意圖 48
圖 5.1 模擬實驗設置示意圖 51
圖 5.2 PSO模擬實驗 52
圖 5.3 ABC模擬實驗 52
圖 5.4 小遮蔽物無自適應搜尋框測試 56
圖 5.5 大遮蔽物無自適應搜尋框測試 57
圖 5.6 大型遮蔽物加了自適應搜尋框測試 58
圖 5.7 亮度正常 59
圖 5.8 亮度偏暗 60
圖 5.9 實驗動作 62
圖 5.10 多目標追蹤 63

表目錄
頁次
表一疊代次數10次與10個解的模擬測試 52
表二疊代次數20次與10個解的模擬測試 53
表三疊代次數30次與10個解的模擬測試 53
表四疊代次數10次與20個解的模擬測試 53
表五疊代次數20次與20個解的模擬測試 53
表六疊代次數30次與20個解的模擬測試 54
表七 ABC演算法模擬測試 54
表八 ABC亮度誤差 60
表九 PSO演算法實際測試 62
表十 ABC演算法實際測試 62

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指導教授

鍾鴻源(Hung-Yuan Chung)

審核日期

2015-8-18

推文