  Visual Object tracking research has undergone significant improvement in the
past few years. The emergence of tracking by detection approach in tracking
paradigm has been quite successful in many ways. Recently, deep convolutional
neural networks have been extensively used in most successful trackers. Yet,
the standard approach has been based on correlation or feature selection with
minimal consideration given to motion consistency. Thus, there is still a need
to capture various physical constraints through motion consistency which will
improve accuracy, robustness and more importantly rotation adaptiveness.
Therefore, one of the major aspects of this paper is to investigate the outcome
of rotation adaptiveness in visual object tracking. Among other key
contributions, the paper also includes various consistencies that turn out to
be extremely effective in numerous challenging sequences than the current
state-of-the-art.
