Human pose estimation from monocular images: A comprehensive survey

W Gong, X Zhang, Jordi Gonzalez Sabate, A Sobral, T Bouwmans, C Tu, E Zahzah

Research output: Contribution to journalArticleResearch

83 Citations (Scopus)


© 2016 by the authors; licensee MDPI, Basel, Switzerland. Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
Original languageEnglish
Issue number12
Publication statusPublished - 1 Dec 2016


  • Bottom-up methods
  • Discriminative methods
  • Generative methods
  • Human body models
  • Human pose estimation
  • Top-down methods


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