基于奇异狄拉克δ势的退火加权QPSO粒子滤波器动态跟踪学习
Learning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential
摘要：
针对视觉跟踪问题，提出了一种改进的全连接量子粒子群优化算法（QPSO）和一种基于记忆引导的进化粒子滤波算法。建议更新步骤使用与最近存储器中遇到的速度成比例的重要性权重来限制可能的感兴趣区域内的群移动。QPSO的reservation计划使用一个健身加权平均最佳更新偏向的群体对最适合的粒子部分，同时也采用了模拟退火算子，以避免低于标准杆微调在后期的迭代过程中。通过使用这样的构造将粒子移动到更接近后验分布的高似然景观，在很大程度上减轻了困扰粒子滤波器的样本分离问题。使用基准序列的实验结果表明，该方法优于竞争的候选跟踪器，如粒子滤波器和传统的粒子群优化粒子滤波的跟踪器性能指标。
英文摘要：
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.
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