  In recent years, Discriminative Correlation Filter (DCF) based methods have
significantly advanced the state-of-the-art in tracking. However, in the
pursuit of ever increasing tracking performance, their characteristic speed and
real-time 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 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.
