0480970 20240111140949.620171107235959.9 85038564492 000418371404098 10.1109/CVPR.2017.514 9 s. E cav_un_epca*0480969978-1-5386-0457-15203-5211PisacatawayIEEE2017 tracking blind deconvolution deblurring temporal superresolution cav_un_auth*0352947 Rozumnyi D. CZ cav_un_auth*0293863 Kotera Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101209 Šroubek Filip UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0209819 Novotný L. CZ cav_un_auth*0075799 Matas J. CZ pdf http://library.utia.cas.cz/separaty/2017/ZOI/sroubek-0480970.pdf 5.2MB cav_un_auth*0338628 GA16-13830S GA ČR CZ The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate with high angular speed. FMOs are very common in sports videos, but are not rare elsewhere. In a single frame, such objects are often barely visible and appear as semi-transparent streaks. A method for the detection and tracking of FMOs is proposed. The method consists of three distinct algorithms, which form an efficient localization pipeline that operates successfully in a broad range of conditions. We show that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance. The proposed method is evaluated on a new annotated dataset. The results show that existing trackers are inadequate for the problem of FMO localization and a new approach is required. Two applications of localization, temporal superresolution and highlighting, are presented. cav_un_auth*0352949 The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 20170721 20170726 Honolulu US JD 20000 20200 20205 2018 5 4 A hod 4ah 20231122142802.5 PO RVO:67985556 http://hdl.handle.net/11104/0276746 cav_un_auth*0331345 FEL Czech Technical University FET CTU CZ 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS S n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Engineering Electrical Electronic 2017 Soubory v repozitáři: sroubek-0480970.pdf 85038564492 SCOPUS PUBMED 000418371404098 WOS pdf 5.2MB cav_un_epca*0480969 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 978-1-5386-0457-1 5203 5211 Pisacataway IEEE 2017