  Robust and accurate scale estimation of a target object is a challenging task
in visual object tracking. Most existing tracking methods cannot accommodate
large scale variation in complex image sequences and thus result in inferior
performance. In this paper, we propose to incorporate a novel criterion called
the average peak-to-correlation energy into the multiresolution translation
filter framework to obtain robust and accurate scale estimation. The resulting
system is named SITUP: Scale Invariant Tracking using Average
Peak-to-Correlation Energy. SITUP effectively tackles the problem of fixed
template size in standard discriminative correlation filter based trackers.
Extensive empirical evaluation on the publicly available tracking benchmark
datasets demonstrates that the proposed scale searching framework meets the
demands of scale variation challenges effectively while providing superior
performance over other scale adaptive variants of standard discriminative
correlation filter based trackers. Also, SITUP obtains favorable performance
compared to state-of-the-art trackers for various scenarios while operating in
real-time on a single CPU.
