  Correlation filter (CF) based tracking algorithms have demonstrated favorable
performance recently. Nevertheless, the top performance trackers always employ
complicated optimization methods which constraint their real-time applications.
How to accelerate the tracking speed while retaining the tracking accuracy is a
significant issue. In this paper, we propose a multi-level CF-based tracking
approach named MLCFT which further explores the potential capacity of CF with
two-stage detection: primal detection and oriented re-detection. The cascaded
detection scheme is simple but competent to prevent model drift and accelerate
the speed. An effective fusion method based on relative entropy is introduced
to combine the complementary features extracted from deep and shallow layers of
convolutional neural networks (CNN). Moreover, a novel online model update
strategy is utilized in our tracker, which enhances the tracking performance
further. Experimental results demonstrate that our proposed approach
outperforms the most state-of-the-art trackers while tracking at speed of
exceeded 16 frames per second on challenging benchmarks.
