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In this paper, we tackle the tracking problem from a fusion angle and propose a disagreement-based approach. While most existing fusion-based tracking algorithms work on different features or parts, our approach can be built on top of nearly any existing tracking systems by exploiting their disagreements. In contrast to assuming multi-view features or different training samples, we utilize existing well-developed tracking algorithms, which themselves demonstrate intrinsic variations due to their design differences. We present encouraging experimental results as well as theoretical justification of our approach. On a set of benchmark videos, large improvements (20% ~40%) over the state-of-the-art techniques have been observed.