结合部件分割和粒子滤波的3D人体运动跟踪

3D Human Motion Tracking Combining Part Segmentation and Particle Filter

尽管随机采样降低了陷入局部极值的风险,但不能保证收敛到全局最优.为此提出了一个将人体部件分割算法嵌入到粒子滤波框架的人体运动跟踪系统.首先使用Condensation算法传播并评估粒子,然后利用基于期望最大化的部件分割算法迭代更新粒子.在迭代过程中,从采样粒子推导的姿态用于部件分割,分割结果用于确定粒子分布,使粒子逐渐接近高似然区域,从而提高找到全局最优的概率并降低采样粒子数.在HumanEva-II数据库上的测试结果表明了文中系统的有效性,且对比实验结果也优于Condensation算法和退火粒子滤波.

In spite of the fact that the stochastic sampling decreases the risk to be trapped in a local extremum,the global optimum can not be guaranteed in the stochastic sampling methods.In this paper,we propose a human motion tracking system embedding body part segmentation in a particle filter framework.Our system uses Condensation algorithm to propagate and evaluate particles,which are then iteratively updated with expectation maximization based body part segmentation algorithm.During the iteration,the body pose information derived from particles is used for partial segmentation.Then the particle distribution is specified by the segmentation results.This strategy steers the particle distribution towards regions with a high likelihood and increases the chance to find the global optimum with fewer particles.The experimental results on HumanEva-II dataset illustrate the effectiveness of our approach.The performance of our proposed method is better than that of Condensation algorithm and annealing particle filter.