Abstract
In recent years, Discriminative Correlation Filter (DCF)
based methods have significantly advanced the state-of-theart in tracking. However, in the pursuit of ever increasing
tracking performance, their characteristic speed and realtime capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting. In
this work, we tackle the key causes behind the problems of
computational complexity and over-fitting, with the aim of
simultaneously improving both speed and performance.
We revisit the core DCF formulation and introduce: (i) a
factorized convolution operator, which drastically reduces
the number of parameters in the model; (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples; (iii) a conservative model
update strategy with improved robustness and reduced complexity. We perform comprehensive experiments on four
benchmarks: VOT2016, UAV123, OTB-2015, and TempleColor. When using expensive deep features, our tracker provides a 20-fold speedup and achieves a 13.0% relative gain
in Expected Average Overlap compared to the top ranked
method [12] in the VOT2016 challenge. Moreover, our fast
variant, using hand-crafted features, operates at 60 Hz on a
single CPU, while obtaining 65.0% AUC on OTB-2015.