本篇論文提出一種新穎的線上物件追蹤之特徵選取演算法。所提出的演算法藉由考慮特徵之間的相關性達到了比之前的研究更好的強健性。我們首先將物件和背景的像素當作是訓練分類器的樣本，然後將特徵選取之問題轉化為找出待選取特徵之分類準確的子集合，並將被選取之特徵結合成一張物件和背景之間可辨性較好的複合物件機率圖，最後在這張複合物件機率圖上執行物件之追蹤。藉由使用Adaboost演算法，我們在每一次迴圈執行過程中選出一個最能補足先前所選出的特徵在分類上不足部分的特徵，並且將這些被選取的特徵之物件機率圖以線性組合結合之得到複合物件機率圖。我們使用了橢圓模型來表示追蹤物體之輪廓以進一步提升追蹤之效果。並且我們提出了一組線上特徵有效性檢查機制來監測之前所選出的特徵，只有在之前所選出的特徵已經不足以代表目前的物件和背景之顏色分佈時才重新選取特徵。我們將所提出的特徵選取演算法和mean-shift追蹤演算法結合，所得到的實驗結果顯示我們提出的特徵選取演算法可以達到非常良好的追蹤結果。

We present a novel online tracking feature selection algorithm in this thesis. This algorithm performs more robust than the previous works by taking the correlation between features into consideration. Pixels of object/background regions are first treated as training samples. The feature selection problem is then modeled as finding a good subset of features and constructing a compound likelihood image with better discriminability for the tracking process. By adopting the AdaBoost algorithm, we iteratively select one best feature which compensate the previous selected features and linearly combine the set of corresponding likelihood images to obtain the compound likelihood image. Ellipse fitting is adopted to further improve the overall performance of tracking. We also propose an online feature validity test to monitor the features and only conduct feature selection when the selected features are not representative. We include the proposed algorithm into the mean shift based tracking system. Experimental results demonstrate that the proposed algorithm achieve very promising results.

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