DeepPTZ: Deep Self-Calibration for PTZ Cameras

Cited 19 time in webofscience Cited 20 time in scopus
  • Hit : 129
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorZhang, Chaoningko
dc.contributor.authorRameau, Francoisko
dc.contributor.authorKim, Junsikko
dc.contributor.authorArgaw, Dawit Murejako
dc.contributor.authorBazin, Jean-Charlesko
dc.contributor.authorKweon, In-Soko
dc.date.accessioned2020-12-19T02:30:20Z-
dc.date.available2020-12-19T02:30:20Z-
dc.date.created2020-12-01-
dc.date.created2020-12-01-
dc.date.issued2020-03-
dc.identifier.citationIEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp.1030 - 1038-
dc.identifier.issn2472-6737-
dc.identifier.urihttp://hdl.handle.net/10203/278750-
dc.description.abstractRotating and zooming cameras, also called PTZ (Pan-Tilt-Zoom) cameras, are widely used in modern surveillance systems. While their zooming ability allows acquiring detailed images of the scene, it also makes their calibration more challenging since any zooming action results in a modification of their intrinsic parameters. Therefore, such camera calibration has to be computed online; this process is called self-calibration. In this paper, given an image pair captured by a PTZ camera, we propose a deep learning based approach to automatically estimate the focal length and distortion parameters of both images as well as the rotation angles between them. The proposed approach relies on a dual-Siamese structure, imposing bidirectional constraints. The proposed network is trained on a large-scale dataset automatically generated from a set of panoramas. Empirically, we demonstrate that our proposed approach achieves competitive performance with respect to both deep learning based and traditional state-of-the art methods. Our code and model will be publicly available at https://github.com/ChaoningZhang/DeepPTZ.-
dc.languageEnglish-
dc.publisherWinter Conference on Applications of Computer Vision-
dc.titleDeepPTZ: Deep Self-Calibration for PTZ Cameras-
dc.typeConference-
dc.identifier.wosid000578444801010-
dc.identifier.scopusid2-s2.0-85085474065-
dc.type.rimsCONF-
dc.citation.beginningpage1030-
dc.citation.endingpage1038-
dc.citation.publicationnameIEEE Winter Conference on Applications of Computer Vision, WACV 2020-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationSnowmass Village-
dc.identifier.doi10.1109/WACV45572.2020.9093629-
dc.contributor.localauthorKweon, In-So-
dc.contributor.nonIdAuthorKim, Junsik-
dc.contributor.nonIdAuthorBazin, Jean-Charles-
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 19 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0