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    Title: 以適應性CamShift演算法應用於多攝影機追蹤系統
    Other Titles: An improved camshift algorithm based on adaptive motion estimation for multiple camera systems
    Authors: 劉允中;Liou, Yun-Jung
    Contributors: 淡江大學電機工程學系碩士班
    江正雄
    Keywords: 移動物件追蹤;Camshift演算法;MeanShift演算法;移動估測法;Moving Object Tracking;Modified CamShift;MeanShift;Motion estimation
    Date: 2012
    Issue Date: 2012-06-21 06:48:46 (UTC+8)
    Abstract: 近年來,由於犯罪率的高漲,使得社會各界不得不開始重視保全機制的重要性,無人監控系統便是其中的一種,然而目前的無人監視系統所能提供的資訊相當有限,主要原因在於攝影機是採用固定式的定點監視,因此便會產生監視範圍的死角,在這樣的因素下,即使拍攝到移動物體,亦很難達到近一步的監視效果,在現今的無人監控系統中,無人監控系統具有移動物體偵測及移動物體自動追蹤的功能是十分重要的,因為我們希望的是掌握全盤的影像資訊。
    一般而言,在被監控的場所中,其影像畫面的變化的部分或是有移動物體的影像區塊,其資訊通常是比較有價值且比較重要的,固本系統便是希望能夠做到自動的偵測是否有移動的物體,並且進一步的自動追蹤移動物體。
      鑑於上述,本篇論文結合移動物體偵測及多移動物體自動追蹤與多攝影機協同技術。利用多攝影機協同技術解決固定式攝影機有限的拍攝視角的問題,以及多個移動物件在畫面中產生重疊(Overlap)以及遮蔽(Occlution)等問題;再來利用多移動物體自動追蹤技術分別同時追蹤在畫面上不同的移動物體,然而在同一個畫面上同時追蹤多個移動物體,此時便會產生重疊(Overlap)以及遮蔽(Occlution)等問題,利用上述的多攝影機協同技術可為解決。
    目前在多物件追蹤方法上大致可分為三種,粒子濾波器(Particle Filter)、卡曼濾波器(Kalman Filter)、以及Cam Shift (Continuously Adaptive Mean-Shift),由於我們的系統必須是即時的多攝影機系統,所以對於耗費大量運算時間的方法是不適用的,因此本論文將使用具有一定的準確率,以及演算法較簡單快速的Cam Shift作為多物件追蹤方法上的技術。而Cam Shift較為簡單快速的原因是在對移動物體作預測時使用快速的移動估測,而本論文提出一種適應性的快速移動估測,使Cam Shift追蹤移動物體的準確率提升,進而使多攝影機協同技術解決重疊以及遮蔽方面來的更為準確及有效。實驗證實,自適應CamShift追蹤演算法的準確率比傳統的CamShift追蹤演算法來的更好,不論是在TKU的公開資料庫或是網路上開放資料庫,在最差的測試影像中也都至少有提升23%的準確性,而大部份的測試影像的準確率也都平均在90%左右,系統也都還維持在23FPS的水準。在多攝影機系統中座標轉換的準確性也有所提升,而系統的Frame Rate也還有在12FPS。
    Smart video surveillance has been developed for a long time, and many approaches to track moving objects have been proposed in recent years. The research of good tracking algorithms becomes one of the main streams for the smart video surveillance research. Multiple moving object tracking is a fundamental task on smart video surveillance systems, because it provides a focus of attention for further investigation. Video surveillance using multiple cameras system has attracted increasing interest in recent years. Moving objects occlusion is a key operation using correspondence between multiple cameras for surveillance system.
    In this thesis, the current state-of-the-art in moving objects tracker for multiple cameras surveillance has been surveyed. An efficient modified adaptive CamShift structure is proposed to further reduce the computational cost and increase the object tracking information in occlusion image. In this work, a new CamShift approach, directional prediction for adaptive CamShift algorithm, is proposed to improve the tracking accuracy rate.
    According to the characteristic of the center-based motion vector distribution for the real-world video sequence, this thesis employs an adaptive pattern (ASP) search to refine the central area search. Furthermore for estimation in large motion situations, the strategy of the adaptive CamShift search can preserve good performance. Experimental results indicate that the accuracy rate of the adaptive CamShift algorithm is better than that of the CamShift algorithm. Furthermore, the proposed method has given an average accuracy rate of 90%, and the operation speed can reach 12 FPS with frame size of 320
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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