Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19603
標題: 使用邊緣特徵改善CAMShift的物件追蹤方法之效能
Improving the Effectiveness of CAMSHIFT Based on Object Edges
作者: 林易增
Lin, Yi-Tzeng
關鍵字: CAMSHIFT
CAMSHIFT
Object tracking
PCA
物件追蹤
主成分分析
出版社: 資訊科學與工程學系所
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摘要: 物件追蹤是一個被廣泛應用的技巧,許多進一步的應用能夠完成;都需要先使用到物件追蹤作為前置的辨識,例如安全監控,行車輔助系統等等,所以關於物件追蹤也有許多的方法被提出來。 CAMSHIFT(Continuously Adaptive Mean Shift)是一種常被使用的物件追蹤方法,單純使用顏色來當唯一的辨識特徵。它有一定的效果,且複雜度低,不需要大量的運算。但是不論是物件、背景太過複雜,或是物件快速移動的時候,常常會失去它的效果。 本文提取並增加了物件的邊緣特徵,來加強CAMSHIFT的效果。且為了降低比對的成本,先將物件邊緣特徵使用主成分分析 (Principal Component Analysis, PCA)計算過後,得到另一個資料量較少的新特徵植。之後先使用邊緣特徵粗估物件的位置之後,再使用CAMSHIFT做第二次的辨識。合併兩種方法讓辨識、追蹤的正確率提高,並且不會付出太多計算的代價。並且經由實驗驗證效果的確可行。
Object tracking is a widely-used skill in lots of advanced applications, such as robots vision and surveillance systems where object tracking is adopted as preliminary identification. Many tracking methods are proposed. CAMSHIFT (Continuously Adaptive Mean Shift), using color as the only identification feature, is very popular. It can achieve certain result. Moreover, it is not complicated, and does not require a great amount of computation. However, the result is usually not satisfactory when the object or the background is too complex, or when the object is moving too quickly. In this thesis, we improve the effectiveness of CAMSHIFT by adding another feature, edges of the object. In order to reduce the cost of computation, the first step is to use PCA (Principal Component Analysis) to screen the data and reduce the amount of data. PCA data matching is used to find the object in the screen. And then we use CAMSHIFT to do the second matching. The experimental results show that, by combining the two methods, edge feature and CAMSHIFT, we improve the accuracy of object identification and tracking at an affordable cost.
URI: http://hdl.handle.net/11455/19603
其他識別: U0005-1307200916560500
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1307200916560500
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