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This paper proposes an evolutionary Particle Filter with a memory guidedproposal step size update and an improved, fully-connected Quantum-behavedParticle Swarm Optimization (QPSO) resampling scheme for visual trackingapplications. The proposal update step uses importance weights proportional tovelocities encountered in recent memory to limit the swarm movement withinprobable regions of interest. The QPSO resampling scheme uses a fitnessweighted mean best update to bias the swarm towards the fittest section ofparticles while also employing a simulated annealing operator to avoid subparfine tune during latter course of iterations. By moving particles closer tohigh likelihood landscapes of the posterior distribution using such constructs,the sample impoverishment problem that plagues the Particle Filter is mitigatedto a great extent. Experimental results using benchmark sequences imply thatthe proposed method outperforms competitive candidate trackers such as theParticle Filter and the traditional Particle Swarm Optimization based ParticleFilter on a suite of tracker performance indices.

Learning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential