改良式粒子群方法之影像追蹤系統應用

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姓名

顏淯翔(Yu-shiang Yan) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

改良式粒子群方法之影像追蹤系統應用
(Visual Tracking System Based on Improved PSO)

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摘要(中)

本論文中，我們提出了一種改良式粒子群演算法，名為單維搜索分工式粒子群演算法(Particle swarm optimization with one dimension multi-modes, ODMPSO)，並應用ODMPSO演算法於影像追蹤系統。標準粒子群演算法中每一個體使用相同的移動方程式，而本文所提的方法，能使粒子根據其位置狀態選擇其移動方程式進行位置的更新。在粒子群最佳化初期，透過特殊的單維搜索機制，讓粒子可以更有效的從局部探索開始，逐漸演化，等到集中收斂至一階段後，再使用分群機制，根據其粒子位置分別置入數個子群中，在子群中的粒子根據其對應的四種模式進行速度更新，以求迅速的把其它個體帶往全域最佳解。本文並利用ODMPSO演算法提升影像追蹤系統上的效能，獲取更好的辨識率與更快的迭代速度。由於我們得知在傳統的高斯混合模型背景相減法(Gaussian mixture model background subtraction)裡，所使用的迭代方式是使用期望值最大化演算法(Expectation Maximization , EM)，而此方法在進行迭代時，緩慢的收斂速度，往往影響了即時的影像辨識系統之實用性，所以本文採取ODMPSO演算法來提升收斂速度，以防止耗費大量的運算，減少系統的運算的複雜度。從實驗結果證實，所提出的ODMPSO可以得到較佳的平均值(Mean)、標準差(Standard deviation)與辨識率，並且能大幅地提升系統的收斂速度，所以證實所提出的演算法的確能有效地增進影像追蹤系統的實用性。

摘要(英)

In this thesis, we propose a modified particle swarm optimization algorithm which is called particle swarm optimization with one dimension multi-modes (ODMPSO). The proposed ODMPSO which is different from standard PSO algorithm is moving functions. In ODMPSO method, the particles can be adaptively searched by their environment. There are five modes in ODMPSO method. Each mode has its own specific optimizations. Finally, these modes makes the particles more easily and quickly find the results. Afterwards, we propose a Gaussian mixture model based on ODMPSO (GMM-ODMPSO) method in a visual tracking system. The GMM-ODMPSO method will accelerate the convergence rate of creating the GMM background model and the system also improves the detection of moving targets. The experimental results show that the proposed GMM background model obtains better recognition rate. As seen in the experiments, the GMM-ODMPSO method is a 48% improvement over the computing time, 88% over the convergence rate, and the recognition rate is almost the same as the traditional GMM background model. In the results, we can see our proposed method is more effective.

關鍵字(中)

★ 粒子群演算法
★ 影像追蹤系統
★ 高斯混合模型背景相減法

關鍵字(英)

★ particle swarm optimization algorithm
★ visual tracking system
★ Gaussian mixture model

論文目次

目錄
摘要 I
Abstract II
第一章緒論 1
1.1 研究背景及動機 1
1.2 研究貢獻及方式 1
1.3 本論文流程架構 3
第二章文獻探討 4
2.1 背景回顧 4
2.2 光流法 4
2.3 運動能量法 5
2.4 背景相減法 7
2.5 適應性背景相減法 9
第三章提出改良式粒子群最佳化方法暨模擬 13
3.1 人工智慧演化最佳化方法 13
3.2 傳統粒子群演算法背景介紹 13
3.3 傳統粒子群演算法架構 14
3.4 傳統粒子群演算法改進方法 16
3.4.1 低差異序列法 16
3.4.2 單維度搜索機制 18
3.4.3建立子群與切換多工搜索模式 22
3.5 模擬實驗結果 27
3.5.1 測試函數10維之結果 31
3.5.2 測試函數30維之結果 36
第四章實現改良式粒子群演算法於高斯混合模型背景相減法之架構 40
4.1 視覺追蹤系統 40
4.2 高斯混合模型之背景相減法 45
4.2.1 高斯混合模型簡介 46
4.2.2 高斯機率密度函數的初始參數估測 48
4.2.3 像素點的歸類 50
4.3 ODMPSO應用於高斯混合模型 52
4.3.1 ODMPSO應用於高斯混合模型-粒子初始化 52
4.3.2 ODMPSO應用於高斯混合模型-參數設定 54
4.3.3 ODMPSO應用於高斯混合模型-適應函數設定 55
第五章改良式粒子群演算法於高斯混合模型背景相減法之實驗結果分析 56
5.1 實驗介紹 56
5.2 實驗結果 56
第六章論文總結與未來發展 63
6.1 論文總結 63
6.2未來發展 64
參考文獻 65

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指導教授

莊堯棠(Yau-tang Juang)

審核日期

2014-7-4

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