  This paper proposes an evolutionary Particle Filter with a memory guided
proposal step size update and an improved, fully-connected Quantum-behaved
Particle Swarm Optimization (QPSO) resampling scheme for visual tracking
applications. The proposal update step uses importance weights proportional to
velocities encountered in recent memory to limit the swarm movement within
probable regions of interest. The QPSO resampling scheme uses a fitness
weighted mean best update to bias the swarm towards the fittest section of
particles while also employing a simulated annealing operator to avoid subpar
fine tune during latter course of iterations. By moving particles closer to
high likelihood landscapes of the posterior distribution using such constructs,
the sample impoverishment problem that plagues the Particle Filter is mitigated
to a great extent. Experimental results using benchmark sequences imply that
the proposed method outperforms competitive candidate trackers such as the
Particle Filter and the traditional Particle Swarm Optimization based Particle
Filter on a suite of tracker performance indices.
