Abstract
Human pose estimation from monocular video streams is a challenging problem. Much of the work on this problem has focused on developing inference algorithms and probabilistic prior models based on learned measurements. Such algorithms face challenges in generalisation beyond the learned dataset. We propose an interactive model-based generative approach for estimating the human pose from uncalibrated monocular video in unconstrained sports TV footage. Belief propagation over a spatio-temporal graph of candidate body part hypotheses is used to estimate a temporally consistent pose between user-defined keyframe constraints. Experimental results show that the proposed generative pose estimation framework is capable of estimating pose even in very challenging unconstrained scenarios.
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Fastovets, M., Guillemaut, JY., Hilton, A. (2014). Estimating Athlete Pose from Monocular TV Sports Footage. In: Moeslund, T., Thomas, G., Hilton, A. (eds) Computer Vision in Sports. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-09396-3_8
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DOI: https://doi.org/10.1007/978-3-319-09396-3_8
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