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The Bag of Words (BoW) model is one of the most popular and effective image representation methods and has been drawn increasing interest in computer vision filed. However, little attention is paid on it in visual tracking. In this paper, a visual tracking method based on Bag of Superpixels (BoS) is proposed. In BoS, the training samples are oversegmented to generate enough superpixel patches. Then K-means algorithm is performed on the collected patches to form visual words of the target and a superpixel codebook is constructed. Finally the tracking is accomplished via searching for the highest likelihood between candidates and codebooks within Bayesian inference framework. In this process, an effective updating scheme is adopted to help our tracker resist occlusions and deformations. Experimental results demonstrate that the proposed method outperforms several state-of-the-art trackers.