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Robust and accurate scale estimation of a target object is a challenging taskin visual object tracking. Most existing tracking methods cannot accommodatelarge scale variation in complex image sequences and thus result in inferiorperformance. In this paper, we propose to incorporate a novel criterion calledthe average peak-to-correlation energy into the multiresolution translationfilter framework to obtain robust and accurate scale estimation. The resultingsystem is named SITUP: Scale Invariant Tracking using AveragePeak-to-Correlation Energy. SITUP effectively tackles the problem of fixedtemplate size in standard discriminative correlation filter based trackers.Extensive empirical evaluation on the publicly available tracking benchmarkdatasets demonstrates that the proposed scale searching framework meets thedemands of scale variation challenges effectively while providing superiorperformance over other scale adaptive variants of standard discriminativecorrelation filter based trackers. Also, SITUP obtains favorable performancecompared to state-of-the-art trackers for various scenarios while operating inreal-time on a single CPU.

SITUP: Scale Invariant Tracking using Average Peak-to-Correlation Energy