  Different layers in CNNs provide not only different levels of abstraction for
describing the objects in the input but also encode various implicit
information about them. The activation patterns of different features contain
valuable information about the stream of incoming images: spatial relations,
temporal patterns, and co-occurrence of spatial and spatiotemporal (ST)
features. The studies in visual tracking literature, so far, utilized only one
of the CNN layers, a pre-fixed combination of them, or an ensemble of trackers
built upon individual layers. In this study, we employ an adaptive combination
of several CNN layers in a single DCF tracker to address variations of the
target appearances and propose the use of style statistics on both spatial and
temporal properties of the target, directly extracted from CNN layers for
visual tracking. Experiments demonstrate that using the additional implicit
data of CNNs significantly improves the performance of the tracker. Results
demonstrate the effectiveness of using style similarity and activation
consistency regularization in improving its localization and scale accuracy.
