Abstract
Correlation filter (CF) based trackers are currently
ranked top in terms of their performances. Nevertheless,
only some of them, such as KCF [26] and MKCF [48], 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 data sets show that our method is superior to
state-of-the-art algorithms for target objects of small move
at very high speed