使用空間與顏色特徵的平均移動演算法於物件大小與方位追蹤

Abstract

本論文中發展了一個以空間和顏色為基礎的平均移動演算法。其中以空間中顏色分佈的相對資訊和顏色的特徵來定義物件的模型，並以新的相似度函數發展出新的平均移動演算法來做物件追蹤，為了要使物件追蹤的效果更穩健，針對不同的特徵做了實驗並選出使追蹤效果最好的顏色特徵，接著並在演算法中加入了以背景資訊而建立的權重值，使得演算法具有更好的穩定性。而為了解決在物件追蹤中常遇到的物件大小與方位的問題，我們使用了主成分分析的方法來估測物件的方位，並以主成分分析所延伸而來的演算法來估計物件的大小，而此方法確實可以自動更新物件的大小與方位。在最後的實驗中則可以看出此追蹤演算法可以解決部份遮蔽和物件變形的問題，且在複雜背景下仍具有良好的即時追蹤效能。

In this thesis, we propose the new mean-shift tracking algorithms based on a new similarity measure function. The joint spatial-color feature is used as our basic model elements. The target image is modeled with the kernel density estimation and we use the concept of expectation of the estimated kernel density to develop the new similarity measure functions. With these new similarity measure functions, two new similarity-based mean-shift tracking algorithms were derived. To enhance the robustness, we add the weighted-background information to the proposed mean-shift tracking algorithm. In order to solve the deformation problem, the principal component analysis method is used to update the orientation of the tracking object, and a simple method is elaborated to monitor the scale of the object. The results of the experiments show that the new similarity-based tracking algorithms are real-time and can track the moving object correctly, and update the orientation and scale of the object automatically.