论文标题
具有3D卡箱模型和新颖的确定性重采样策略的基于粒子滤波器的单眼跟踪
Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy
论文作者
论文摘要
无标记的人类运动跟踪的挑战是搜索空间的高维度。因此，在搜索空间中有效探索具有重要意义。在本文中，提出了一种捕获上身运动跟踪的运动捕获算法。所提出的系统跟踪基于单眼轮廓匹配的人类运动，并建立在分层粒子过滤器的顶部，其中应用了新型的确定性重新采样策略（DRS）。通过惯性传感器系统测量的地面真实数据进行定量评估所提出的系统。此外，我们将DRS与分层的重采样策略（SRS）进行了比较。在实验中显示的，DR以相同量的颗粒的表现优于SRS。此外，创建了一个新的3D铰接式人类上半身模型，该模型是创建了名称3D卡盒模型的，并且被证明可以成功地进行运动跟踪。实验表明，所提出的系统可以在不自钉的情况下坚固地跟踪上身运动。向相机的动作也可以很好地跟踪。
The challenge of markerless human motion tracking is the high dimensionality of the search space. Thus, efficient exploration in the search space is of great significance. In this paper, a motion capturing algorithm is proposed for upper body motion tracking. The proposed system tracks human motion based on monocular silhouette-matching, and it is built on the top of a hierarchical particle filter, within which a novel deterministic resampling strategy (DRS) is applied. The proposed system is evaluated quantitatively with the ground truth data measured by an inertial sensor system. In addition, we compare the DRS with the stratified resampling strategy (SRS). It is shown in experiments that DRS outperforms SRS with the same amount of particles. Moreover, a new 3D articulated human upper body model with the name 3D cardbox model is created and is proven to work successfully for motion tracking. Experiments show that the proposed system can robustly track upper body motion without self-occlusion. Motions towards the camera can also be well tracked.
