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Kinematics-based tracking of human walking in monocular video sequences

机译：基于运动学的单眼视频序列中的人类步行跟踪

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摘要

Human tracking is currently one of the most active research topics in computer vision. This paper proposed a kinematics-based approach to recovering motion parameters of people walking from monocular video sequences using robust image matching and hierarchical search. Tracking a human with unconstrained movements in monocular image sequences is extremely challenging. To reduce the search space, we design a hierarchical search strategy in a divide-and-conquer fashion according to the tree-like structure of the human body model. Then a kinematics-based algorithm is proposed to recursively refine the joint angles. To measure the matching error, we present a pose evaluation function combining both boundary and region information. We also address the issue of initialization by matching the first frame to six key poses acquired by clustering and the pose having minimal matching error is chosen as the initial pose. Experimental results in both indoor and outdoor scenes demonstrate that our approach performs well.
机译：人工跟踪是当前计算机视觉中最活跃的研究主题之一。本文提出了一种基于运动学的方法，该方法使用健壮的图像匹配和分层搜索从单眼视频序列中恢复行走的人的运动参数。跟踪单眼图像序列中不受限制的运动的人非常具有挑战性。为了减少搜索空间，我们根据人体模型的树状结构设计了分而治之的分层搜索策略。然后提出了一种基于运动学的算法来递归地改善关节角度。为了测量匹配误差，我们提出了一种结合边界和区域信息的姿态评估函数。我们还通过将第一帧与通过聚类获得的六个关键姿势进行匹配来解决初始化问题，并选择具有最小匹配误差的姿势作为初始姿势。在室内和室外场景下的实验结果表明，我们的方法效果良好。

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