  We propose a novel particle filter for convolutional-correlation visual
trackers. Our method uses correlation response maps to estimate likelihood
distributions and employs these likelihoods as proposal densities to sample
particles. Likelihood distributions are more reliable than proposal densities
based on target transition distributions because correlation response maps
provide additional information regarding the target's location. Additionally,
our particle filter searches for multiple modes in the likelihood distribution,
which improves performance in target occlusion scenarios while decreasing
computational costs by more efficiently sampling particles. In other
challenging scenarios such as those involving motion blur, where only one mode
is present but a larger search area may be necessary, our particle filter
allows for the variance of the likelihood distribution to increase. We tested
our algorithm on the Visual Tracker Benchmark v1.1 (OTB100) and our
experimental results demonstrate that our framework outperforms
state-of-the-art methods.
