臺灣博碩士論文加值系統

物件追蹤為電腦視覺領域重要的議題，可應用於監控系統與人機介面。如何準確估測物件大小並採用合適的物件特徵，以增進追蹤準確度，實為重要的課題。本論文以人類頭部為追蹤目標，以粒子濾波法為基礎，建立適用於非線性與非高斯機率描述的機率狀態轉換與量測的系統。我們將偵測機制整合使追蹤系統，並提出在追蹤的過程中，依據追蹤結果與目標物件的顏色相似度，啟動以不同特徵為基礎的頭部定位系統之方案，重置追蹤系統的目標物件顏色資訊和目前畫面的頭部大小。實驗結果顯示，當人頭部隨意運動，快速移動和對攝影機有距離遠近改變時，本系統仍可達成不錯的追蹤準確性。

Object tracking is an important technique in computer vision, and it can be applied in applications such as visual surveillance and human-robot interaction. How to estimate object scale accurately and choose proper feature to improve tracking accuracy is an important issue. In this paper, our tracking system tracks human heads with particle filter with non-linear and non-Gaussian state transition and measurement. We integrate head detection into tracking system and propose to start head localization with various features based on color similarity of tracking measurement. We reset target color histogram and head scale if needed. Experimental results show that our head tracking system has good tracking accuracy under human regular motion, fast motion and distance variation between the target and the camera.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 XII
第一章 緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 研究方法 2
1.4 論文架構 3
第二章 物件偵測 4
2.1 物件特徵 4
2.2 物件表示法 6
2.3 物件偵測發展現況 8
2.4 Adaboost人臉偵測演算法 10
2.4.1 Haar-like特徵擷取 11
2.4.2 Adaboost訓練演算法 14
2.4.3 串聯式分類器 15
2.5 總結 16
第三章 物件追蹤 17
3.1 剪影追蹤(Silhouette tracking) 17
3.2 核心追蹤(Kernel tracking) 19
3.2.1平均移動(mean shift)追蹤演算法 20
3.2.2 橢圓追蹤演算法 22
3.3 點追蹤(Point tracking) 23
3.3.1 貝氏濾波法(Bayesian filter) 24
3.3.2 卡爾曼濾波法(Kalman filter) 25
3.3.3 粒子濾波法(Particle filter) 26
3.4 總結 31
第四章 以粒子濾波法為基礎之頭部追蹤系統 32
4.1 應用於物件追蹤以顏色為基礎之適應性粒子濾波法 32
4.1.1 系統架構 32
4.1.2 應用於物件追蹤以顏色為基礎之粒子濾波法 33
4.1.3 目標物件顏色模型更新與物件消失處理 38
4.2 我們提出以粒子濾波法為基礎之改良式頭部追蹤系統 39
4.2.1 系統架構 40
4.2.2 系統狀態與特徵分析 42
4.2.3偵測重置機制與目標物件顏色模型更新 46
4.2.4 結合於追蹤系統之頭部定位系統 48
4.2.5 顏色樣板偵測 49
4.3 總結 50
第五章 實驗結果 51
5.1 實驗環境與測試影片 51
5.2 系統追蹤效能 52
5.2.1追蹤系統的準確度 53
5.2.2 顏色相似度變化 75
5.2.3 系統的計算複雜度 82
第六章 結論與未來展望 91
6.1 結論 91
6.2 未來展望 91
參考文獻 92

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