基于多模板配准的人体上半身三维运动跟踪

3D Human Upper Body Pose Tracking Based on Multi-Template Register

单目视频下的人体三维运动跟踪过程中,二维肢体模板由于遮挡而无法配准,从而造成跟踪丢失。针对这一问题,建立人体肢体的三维纹理模型用于人体运动跟踪。首先在初始帧中得到纹理模型在直立姿态下的二维模板及各像素点的局部坐标,在其它帧中利用像素灰度一致性约束优化进行跟踪,同时由跟踪结果生成新的二维模板,形成一个模板库。随着跟踪帧数增加,模板库动态更新,最终跟踪结果由模板库中各模板配准结果中的最优值决定。实验结果表明,与单一二维模板配准算法相比,多模板匹配能够克服由于肢体自旋转而造成的遮挡,取得了更好的跟踪结果。

In three-dimensional （3D） human motion tracking from monocular video sequences, twodimensional （2D） body template cannot register accurately due to occlusion, which lead to track fail. To overcome this problem, a 3D texture model is used to track human motion. First, the template of the texture model and the local coordinates of each pixel under straight pose are initialized in the first frame. Then, the human motion is optimized by the pixel intensity consistency constraint, and a new 2D template is obtained by the tracked result. To contain the latest body information, the oldest template in the collection is dumped while a new template arrives, and the finally tracking result is determined by the output of template with the highest similarity. The experimental results demonstrate that the proposed algorithm is more effective in solving occlusion caused by segment self-rotation than the single 2D template matching method.