Abstract
Human pose estimation from monocular images is one of the most significant aspects of modern computer vision tasks and its application demand is still increasing in such areas as automatic images indexing or human activity recognition from video. Among many approaches applied in these areas the one based on pose estimation gives, beyond all doubts, one of the most powerful representation of human on the picture in sense of sparsity and semantics. In this paper we provide a detailed survey of the most efficient methods in 2D pose estimation domain as well as the test results of selected methods on the LSP dataset, which is commonly used by state-of-the-art works.
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Acknowledgements
This work has been supported by the National Centre for Research and Development (project UOD-DEM-1-183/001 “Intelligent video analysis system for behavior and event recognition in surveillance networks”).
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Bąk, A., Kulbacki, M., Segen, J., Świątkowski, D., Wereszczyński, K. (2016). Recent Developments on 2D Pose Estimation From Monocular Images. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_43
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