  In this paper, we propose a novel visual tracking framework that
intelligently discovers reliable patterns from a wide range of video to resist
drift error for long-term tracking tasks. First, we design a Discrete Fourier
Transform (DFT) based tracker which is able to exploit a large number of
tracked samples while still ensures real-time performance. Second, we propose a
clustering method with temporal constraints to explore and memorize consistent
patterns from previous frames, named as reliable memories. By virtue of this
method, our tracker can utilize uncontaminated information to alleviate
drifting issues. Experimental results show that our tracker performs favorably
against other state of-the-art methods on benchmark datasets. Furthermore, it
is significantly competent in handling drifts and able to robustly track
challenging long videos over 4000 frames, while most of others lose track at
early frames.
