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Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion.<\/jats:p>","DOI":"10.1007\/s11554-020-01002-w","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T07:02:34Z","timestamp":1599202954000},"page":"1757-1768","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Fast automatic camera network calibration through human mesh recovery"],"prefix":"10.1007","volume":"17","author":[{"given":"Nicola","family":"Garau","sequence":"first","affiliation":[]},{"given":"Francesco G. 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