  This paper presents a novel object tracking method based on approximated
Locality-constrained Linear Coding (LLC). Rather than using a non-negativity
constraint on encoding coefficients to guarantee these elements nonnegative, in
this paper, the non-negativity constraint is substituted for a conventional
$\ell_2$ norm regularization term in approximated LLC to obtain the similar
nonnegative effect. And we provide a detailed and adequate explanation in
theoretical analysis to clarify the rationality of this replacement. Instead of
specifying fixed K nearest neighbors to construct the local dictionary, a
series of different dictionaries with pre-defined numbers of nearest neighbors
are selected. Weights of these various dictionaries are also learned from
approximated LLC in the similar framework. In order to alleviate tracking
drifts, we propose a simple and efficient occlusion detection method. The
occlusion detection criterion mainly depends on whether negative templates are
selected to represent the severe occluded target. Both qualitative and
quantitative evaluations on several challenging sequences show that the
proposed tracking algorithm achieves favorable performance compared with other
state-of-the-art methods.
