In this paper, we revisit the problem of visual tracking by introducing a novel low-dimensional descriptor based on gradient distribution and specifically focus our attention on the problem of low-resolution and long-term visual tracking. We show that our tracking solution empowered by our proposed descriptor can effectively address the existing challenges in low-resolution and long-term visual tracking. Compared to the existing descriptors, the proposed method provides better robustness against local geometric and photometric variations. It adopts a new approach for aggregating information in a local neighborhood such that the sensitivity of the descriptor to noise and unreliable texture information is reduced. Integrating the proposed descriptor into a correlation-based tracking framework results in a robust and fast visual tracker. An extensive set of experiments on a number of large-scale benchmark datasets shows that the proposed method outperforms the state-of-the-art methods on low-resolution and long-term challenges, while achieving state-of-the-art performance in generic tracking.

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