Articulated Human Motion Tracking with HPSO
Articulated Human Motion Tracking with HPSO
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DOI：
10.5220/0001804505310538
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发表时间：
2009
影响因子：
3.7
通讯作者：
V. John;S. Ivekovic;E. Trucco
中科院分区：
文献类型：
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作者：
V. John;S. Ivekovic;E. Trucco
在本文中，我们解决了全身关节的人体运动跟踪从多视角视频序列中获得的演播室环境。跟踪制定为一个多维的非线性优化和解决粒子群优化（PSO），群体智能算法，近年来已获得普及，由于其能够解决困难的非线性优化问题。我们的跟踪方法旨在解决粒子滤波方法的局限性：它自动初始化，消除了对序列特定运动模型的需要，并通过使用强大的分层搜索算法（HPSO）从临时跟踪发散中恢复。我们定量地比较了HPSO与粒子滤波器（PF）和退火粒子滤波器（APF）的性能。我们的测试结果是使用（Balan等人，2005）比较关节式身体跟踪算法，表明HPSO的姿态估计精度和一致性优于PF，并且与APF相比毫不逊色，在突然和快速运动的序列中优于它。
In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation problems. Our tracking approach is designed to address the limits of particle filtering approaches: it initialises automatically, removes the need for a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We quantitatively compare the performance of HPSO with that of the particle filter (PF) and annealed particle filter (APF). Our test results, obtained using the framework proposed by (Balan et al., 2005) to compare articulated body tracking algorithms, show that HPSO’s pose estimation accuracy and consistency is better than PF and compares favourably with the APF, outperforming it in sequences with sudden and fast motion.