Web of Science: 45 cites, Scopus: 49 cites, Google Scholar: cites,
Human Pose Estimation from Monocular Images : a Comprehensive Survey
Gong, Wenjuan (China University of Petroleum. Department of Computer Science and Technology)
Zhang, Xuena (China University of Petroleum. Department of Computer Science and Technology)
Gonzàlez i Sabaté, Jordi (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Sobral, Andrews (Université de La Rochelle)
Bouwmans, Thierry (Université de La Rochelle)
Tu, Changhe (Shandong University. School of Computer Science and Technology)
Zahzah, El-hadi (Université de La Rochelle)

Data: 2016
Resum: 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 problema into several modules: feature extraction and description, human body models, and modelin 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Human pose estimation ; Human body models ; Generative methods ; Discriminative methods ; Top-down methods ; Bottom-up methods
Publicat a: Sensors (Basel, Switzerland), Vol. 16 No. 12 (November 2016) , art. 1966, ISSN 1424-8220

DOI: 10.3390/s16121966
PMID: 27898003

39 p, 2.3 MB

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