研究生: |
黃塏碩 Kai-shuo Huang |
---|---|
論文名稱: |
在非固定式鏡頭下之物件追蹤-基於顏色與紋理資訊的適應性粒子濾波器追蹤演算法 Object Tracking under a Moving Camera–An Adaptive Color-Texture-based Particle Filter Tracking Algorithm |
指導教授: |
王乃堅
Nai-jian Wang |
口試委員: |
劉昌煥
Chang-huan Liu 鍾順平 Shun-ping Chung 呂學坤 Shyue-Kung Lu 蔡超人 Chau-ren Tsai |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電機工程系 Department of Electrical Engineering |
論文出版年: | 2010 |
畢業學年度: | 98 |
語文別: | 中文 |
論文頁數: | 110 |
中文關鍵詞: | 物件追蹤 、粒子濾波器 、旋轉不變特性 、區域二位元圖形 |
外文關鍵詞: | Object tracking, Particle Filter, Rotation-Invariant, Local Binary Patterns |
相關次數: | 點閱:425 下載:6 |
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近年來,隨著電腦視覺技術的進步及電腦運算能力的提升,移動物件追蹤系統被應用在各種不同的領域上,如:安全監視系統、醫療看護系統等應用。在眾多追蹤演算法中,許多方法容易因為需要背景模型的建立,而使得演算法僅能於固定式攝影鏡頭上做處理,有鑑於此,我們提出了一個新的追蹤演算法,並希望能在非固定式鏡頭所拍攝的影像下,追蹤畫面中任何我們感興趣的物件。
本論文所提出的移動物件追蹤演算法,針對傳統適應性粒子濾波器在適應物件大小上的問題做改善,使用了包含旋轉不變特性的區域二位元圖形的紋理資訊,以及色彩模型資訊做模型的建立,來增加追蹤準確度。並利用我們的遮蔽策略,在目標物件受到部分遮蔽時,能調整參考兩種資訊的權重進行處理;不同於以往的做法,當物件受到完全遮蔽時,改以較大的搜尋範圍及較多的樣本數目來進行目標物件之座標及尺寸的估算,使得物件能在遮蔽狀況結束後還能追蹤到該目標物件。
實驗結果顯示,我們的演算法在物件外表變形或受到外在環境改變影響下,還能進行良好的追蹤處理,並且我們能較原始的適應性粒子濾波器演算法使用較少的樣本數進行物件追蹤處理,在運算速度上亦可擁有不錯的表現。
In the last decade, object tracking systems have been widely applied in many different fields due to the rapid development of computer vision techniques and faster computing ability, such as Surveillance System, Health-Care System. In this field, many approaches require establishing background in preprocessing step. This limits tracking algorithm only be executed under a fixed camera. However, many applications are taking place in a moving camera. Accordingly, we propose a new algorithm to track rigid or non-rigid object by a moving camera.
The proposed tracking algorithm use rotation-invariant texture feature and color feature to increase the tracking correctness. The target is jointly modeled by color and texture information. We adjust the weight of each feature, so it is less sensitive to different circumstances such as partial occlusions. When fully occluded, we extend search region and double the particle number to avoid missing target if the occlusion disappear.
The experimental results reveal that our tracking method can efficiently and successfully track rigid or non-rigid object under appearance and illumination changes. Also, fewer samples are used to achieve better result than the traditional particle filter method.
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