Human Pose Estimation in Videos

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In this paper, we present a method to estimate a sequence of human poses in unconstrained videos. In contrast to the commonly employed graph optimization framework, which is NP-hard and needs approximate solutions, we formulate this problem into a unified two stage tree-based optimization problem for which an efficient and exact solution exists. Although the proposed method finds an exact solution, it does not sacrifice the ability to model the spatial and temporal constraints between body parts in the video frames; indeed it even models the symmetric parts better than the existing methods. The proposed method is based on two main ideas: `Abstraction' and `Association' to enforce the intra- and inter-frame body part constraints respectively without inducing extra computational complexity to the polynomial time solution. Using the idea of `Abstraction', a new concept of `abstract body part' is introduced to model not only the tree based body part structure similar to existing methods, but also extra constraints between symmetric parts. Using the idea of `Association', the optimal tracklets are generated for each abstract body part, in order to enforce the spatiotemporal constraints between body parts in adjacent frames. Finally, a sequence of the best poses is inferred from the abstract body part tracklets through the tree-based optimization. We evaluated the proposed method on three publicly available video based human pose estimation datasets, and obtained dramatically improved performance compared to the state-of-the-art methods.

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Human Pose Estimation in Videos (PDF)
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