臺灣博碩士論文加值系統

物件追蹤（Object tracking）技術在影像處理(Image Processing)及電腦視覺(Computer Vision)的領域中一直受到許多注意。在許多不同的研究及應用領域中都能發現針對不同需求而提出的物件追蹤技術。不同的物件追蹤技術通常針對某種特殊的需要，例如對於準確性的需求、對於反應速度的需求以及對於特別物件例如車輛或人臉追蹤的需求。
本論文提出一個快速物件追蹤技術。經由偵測畫面中物體如大小、形狀等空間特徵以及色彩特徵提供在固定背景的前提下，以犧牲少許準確性換取能夠進行自動快速強固的多物件追蹤演算法。本論文著重於如何藉著利用色彩特徵及空間特徵進行自動物件追蹤，同時可避免由於雜訊、光度以及影像分割失敗造成的錯誤。由於本技術能夠有效排除背景雜訊造成的影響以及處理速度快速的特性，因此可使用於各種需要高反應速度的應用領域。

Object tracking is always an important field both in image processing and computer vision. Literatures of object tracking techniques fulfilling different necessities can be found in different fields of research and application. Usually, different object tracking technique fulfills different necessities, including accuracy, efficiency, or the need of tracking vehicles and human bodies.
In this thesis, we propose an object tracking method which tracks both the color and the spatial feature. By detecting the size of the objects, spatial features and color and by sacrificing some accuracy, we obtain a fast and robust multi-object tracking algorithm. This thesis
emphasizes on analyzing color and spatial space, in order to efficiently avoid tracking error caused by noise, luminance, and failures of segmentation. And since this algorithm can overcome the effect caused by background noise and is efficient enough, it is useful for any
application where short response time is required.

誌謝 ii
摘要 vii
Abstract viii
表次 xi
圖次 xii
使用符號說明 xiv
第1章緒論 1
1.1 研究動機及目的 1
1.2 章節說明 2
第2章相關研究 3
2.1 前言 3
2.2 影像分割與物件擷取 4
2.2.1 影像分割 4
2.2.2 物件擷取 7
2.3 色彩空間 7
2.3.1 RGB色彩空間 8
2.3.2 YUV/YIQ色彩空間 9
2.3.3 CIELab色彩空間 9
2.3.4 HSB/HSV色彩空間 10
2.3.5 HSI/HSL色彩空間 10
2.3.6 色彩分群 11
2.4 以投票為基礎的物件追蹤技術 15
2.4.1 物件特徵擷取 16
2.4.2 建立物件關聯 17
2.4.3 物件關聯修正 19
2.4.4 物件錯誤合併修正 22
2.4.5 物件錯誤分割修正 23
第3章混合色彩資訊快速物件追蹤技術 25
3.1 簡介 25
3.2 混合色彩資訊快速物件追蹤 26
3.2.1 前處理 26
3.2.2 物件特徵色彩 28
3.2.3 關聯偵測及關聯修正 29
3.2.3.1 關聯偵測 29
3.2.3.2 關聯修正 30
3.2.4 分割及合併修正 30
3.2.4.1 合併錯誤修正 31
3.2.4.2 分割錯誤修正 32
第4章實驗結果 34
4.1 物件追蹤 34
4.1.1 影片cr3結果 34
4.1.2 影片road3結果 35
4.1.3 影片hall結果 36
4.2 影像分割錯誤修正 37
4.2.1 錯誤分割修正 37
4.2.2 錯誤合併修正 38
4.3 計算速度 39
第5章討論及未來展望 42
5.1 討論 42
5.2 演算法限制及適用環境 42
5.3 未來展望 43
參考文獻 44

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