  Correlation filter (CF) based trackers are currently ranked top in terms of
their performances. Nevertheless, only some of them, such as
KCF~\cite{henriques15} and MKCF~\cite{tangm15}, are able to exploit the
powerful discriminability of non-linear kernels. Although MKCF achieves more
powerful discriminability than KCF through introducing multi-kernel learning
(MKL) into KCF, its improvement over KCF is quite limited and its computational
burden increases significantly in comparison with KCF. In this paper, we will
introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL
version of CF objective function with its upper bound, alleviating the negative
mutual interference of different kernels significantly. Our novel MKCF tracker,
MKCFup, outperforms KCF and MKCF with large margins and can still work at very
high fps. Extensive experiments on public datasets show that our method is
superior to state-of-the-art algorithms for target objects of small move at
very high speed.
