FAST: Fast and Accurate Scale Estimation for Tracking
Haoyi Ma, Zongli Lin, Scott T. Acton · IEEE Signal Processing Letters · 2019
In visual object tracking, robust and accurate scale estimation of a target is a challenging task. Despite the associated computational expense, existing tracking methods cannot accommodate large scale variations. Here, we propose a scale searching scheme that obtains robust and accurate scale estimation by incorporating a novel and robust criterion, the average peak-to-correlation energy, into a multi-resolution translation filter framework. To address the problem of computational expense, we introduce an expeditious search strategy. The resulting system is named FAST: Fast and Accurate Scale estimation for Tracking. Comprehensive evaluation using the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework can accommodate large scale variation while also yielding computational efficiency.