Distance Map of Various Weights: A new feature for adaptive object tracking
In this paper, we propose a new feature, Distance Map of Various Weights (DMVW) based on distances between rows' textures, to perform tracking. The proposed new feature provides an effective object appearance model which is both illumination-invariant and robust to occlusion. We also develop a 2D PCA based method to effectively evaluate the new feature. We demonstrate the validity of the rows' or column's weights in computing 2D PCA subspaces. To balance the importance of local and global information, we define a coefficient to revise the locality extent of the proposed feature. A new method based on entropy of candidate state evaluation is proposed to select the most discriminative coefficient. Experimental results on challenging video sequences demonstrated the effectiveness of our method.