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:
Nov. 20, 2018
Filed:
Dec. 21, 2015
Hitachi Kokusai Electric Inc., Minato-ku, Tokyo, JP;
Miyako Hotta, Tokyo, JP;
Masanori Miyoshi, Tokyo, JP;
Kazunari Iwanaga, Tokyo, JP;
Mitsue Ito, Tokyo, JP;
Hitachi Kokusai Electric Inc., Tokyo, JP;
Abstract
The present invention provides a crowd monitoring system with which it is possible to obtain a crowd density accurately, irrespective of the congestion state. This crowd monitoring systemis provided with: an image acquiring unitwhich acquires a plurality of images; an arithmetic logic unitand a storage unitwhich stores information relating to relationships between image feature quantities and an object density, acquired in advance, and information relating to relationships between motion feature quantities and the object density. The arithmetic logic unitcomprises: an image feature quantity acquiring unitwhich obtains image feature quantities of objects in the acquired images; a motion line acquiring unitwhich obtains motion lines of the objects in the acquired images; a motion feature quantity acquiring unitwhich obtains motion feature quantities of the objects on the basis of the obtained motion lines; and a crowd density acquiring unitThe arithmetic logic unitis characterized in that it obtains a first estimated density of the objects on the basis of the obtained image feature quantities and the stored relationships between the image feature quantities and the object density, and obtains a second estimated density of the objects on the basis of the obtained motion feature quantities and the stored relationships between the motion feature quantities and the object density.