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 generalization beyond the learned dataset. We propose an interactive model-based generative approach for estimating the human pose in 2D from uncalibrated monocular video in unconstrained sports TV footage without any prior learning on motion captured or annotated data. Belief-propagation over a spatio-temporal graph of candidate body part hypotheses is used to estimate a temporally consistent pose between key-frame constraints. Experimental results show that the proposed generative pose estimation framework is capable of estimating pose even in very challenging unconstrained scenarios.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
