一种自适应的PSO粒子滤波人脸视频跟踪方法
Face Tracking Based on Adaptive PSO Particle Filter
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摘要: 提出了一种自适应的PSO粒子滤波人脸视频跟踪算法。本算法充分利用粒子群算法的寻优能力,使粒子向真实值的后验概率分布移动,同时引入小生境(niche)技术加以改进,构造出多种群特性,使目标分布呈现非线性非高斯特性的多模分布,由此提高对动态系统中最优解动态变化的自适应能力。实验表明,在简单背景匀速运动、复杂背景匀速和变速运动的人脸视频跟踪中,和传统粒子滤波、普通PSO粒子滤波相比,具有良好的跟踪精度和稳定性。Abstract: This paper presents a new adaptive PSO particle filter face tracking algorithm.Our algorithm fully utilizes particle swarm optimization(PSO) ability to make the posterior probability distribution movements of the particle,meanwhile introduces the niche technology to improve the particle diversity,then the target distribution is nonlinear non-Gaussian and multi-mode,thus improves dynamic adaptive ability to the optimal solution of the dynamic system.Experimental results show that our algorithm has a good tracking accuracy and stability when comparing with particle filter and traditional PSO in the simple background,complex uniform and variable motion background.