The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Apr. 21, 2020
Filed:
Dec. 31, 2019
Stradvision, Inc., Pohang-si, KR;
Kye-Hyeon Kim, Seoul, KR;
Yongjoong Kim, Pohang-si, KR;
Hak-Kyoung Kim, Pohang-si, KR;
Woonhyun Nam, Pohang-si, KR;
SukHoon Boo, Anyang-si, KR;
Myungchul Sung, Pohang-si, KR;
Dongsoo Shin, Suwon-si, KR;
Donghun Yeo, Pohang-si, KR;
Wooju Ryu, Pohang-si, KR;
Myeong-Chun Lee, Pohang-si, KR;
Hyungsoo Lee, Seoul, KR;
Taewoong Jang, Seoul, KR;
Kyungjoong Jeong, Pohang-si, KR;
Hongmo Je, Pohang-si, KR;
Hojin Cho, Pohang-si, KR;
Stadvision, Inc., Pohang-si, KR;
Abstract
A learning method for detecting unoccupied parking spaces by using probability distributions on decision points of the unoccupied parking spaces and relational linear segment information on relationships among the decision points is provided. And the method includes steps of: (a) a learning device performing (i) a process of instructing a first CNN to apply a first CNN regression operation to a parking circumstance image, to thereby calculate each of one or more estimated probability distributions, and (ii) a process of instructing a second CNN to apply a second CNN regression operation to the parking circumstance image, to thereby generate estimated relational linear segment information; and (b) the learning device instructing a loss layer to perform (i) a process training parameters in the first CNN by performing backpropagation using a first loss, and (ii) a process of training of parameters in the second CNN by performing backpropagation using a second loss.