Tracking Generic Human Motion via Fusion of Low- and High-Dimensional Approaches
Tracking Generic Human Motion via Fusion of Low- and High-Dimensional Approaches
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DOI：
10.5244/c.25.57
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发表时间：
2013-02
期刊：
影响因子：
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通讯作者：
Yuandong Xu;J. Cui;Huijing Zhao;H. Zha
中科院分区：
文献类型：
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作者：
Yuandong Xu;J. Cui;Huijing Zhao;H. Zha
跟踪一般的人体运动是非常具有挑战性的，由于其高维的状态空间和各种运动类型的参与。为了应对这些挑战，提出了一种融合配方，它集成了低维和高维跟踪方法到一个框架。低维方法通过学习运动模型成功地克服了用可用训练数据跟踪运动的高维问题，但它只适用于特定的运动类型。另一方面，虽然高维方法可以通过直接在姿态空间中采样来恢复运动而无需学习模型，但它缺乏鲁棒性和效率。在该框架内，两个并行的方法，低维和高维，融合通过概率的方法在每个时间步。这种概率融合方法通过集中两种方法各自的优点并解决它们的弱点，确保了系统的整体性能得到改善。实验结果表明，在定性和定量比较后，所提出的方法在跟踪通用人体运动的有效性。
Tracking generic human motion is highly challenging due to its high-dimensional state space and the various motion types involved. In order to deal with these challenges, a fusion formulation which integrates low- and high-dimensional tracking approaches into one framework is proposed. The low-dimensional approach successfully overcomes the high-dimensional problem of tracking the motions with available training data by learning motion models, but it only works with specific motion types. On the other hand, although the high-dimensional approach may recover the motions without learned models by sampling directly in the pose space, it lacks robustness and efficiency. Within the framework, the two parallel approaches, low- and high-dimensional, are fused via a probabilistic approach at each time step. This probabilistic fusion approach ensures that the overall performance of the system is improved by concentrating on the respective advantages of the two approaches and resolving their weak points. The experimental results, after qualitative and quantitative comparisons, demonstrate the effectiveness of the proposed approach in tracking generic human motion.