  This work proposes a novel framework for visual tracking based on the
integration of an iterative particle filter, a deep convolutional neural
network, and a correlation filter. The iterative particle filter enables the
particles to correct themselves and converge to the correct target position. We
employ a novel strategy to assess the likelihood of the particles after the
iterations by applying K-means clustering. Our approach ensures a consistent
support for the posterior distribution. Thus, we do not need to perform
resampling at every video frame, improving the utilization of prior
distribution information. Experimental results on two different benchmark
datasets show that our tracker performs favorably against state-of-the-art
methods.
