Visual object tracking has advanced significantly in recent years. However

variations in appearance because of changes in scale

motion

shape deformation

or occlusion continue to pose challenges to object tracking. Therefore

an efficient appearance representation method plays a key role in improving the robustness of object tracking. In this paper

a tracking method from the perspective of midlevel is proposed. First

superpixel segmentation is performedon training frames

in which the feature set and corresponding confidence values are taken as inputs and the discriminative appearance model is constructed via feature regression. When the tracking frame comes

the feature set is inputted into the model and the confidence of candidate regions is obtained

thereby separating the target from the background. The algorithm is evaluated using public data sets. Experimental results demonstrate that our algorithm can handle appearance changes

such as variations in scale

position

illumination

shape distortion

and occlusion. Compared with state-of-the-art methods

our algorithm performs well in center error and obtains the best result in carScale

subway

and tiger1 sequences

with average center location errors of 12

3 and 21 pixels

respectively. Comparedwith the same type of method

our algorithm is more efficient in all sequences and in 32 times of other method in carScale sequence. Experimental results demonstrate the effectiveness and robustness of our tracking method under appearance changes. Only one kind of feature is applied in the proposed algorithm; thus

better features can be incorporated to further improve the tracking results.