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JACIII Vol.18 No.4 pp. 632-647
doi: 10.20965/jaciii.2014.p0632
(2014)

Paper:

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Estimation of Locations of Densely Distributed Subjects Using NMF with Nonpixel Information

Yusuke Kubo, Masao Kubo, Hiroshi Sato,
and Akira Namatame

National Defense Academy of Japan, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan

Received:
July 21, 2013
Accepted:
March 12, 2014
Published:
July 20, 2014
Creative Commons License
Keywords:
nonnegative matrix factorization (NMF), collective intelligence, social sensing, social mining, GIS
Abstract
We propose a method that uses a large number of digital photographs to produce highly accurate estimates of the locations of subjects that have attracted a crowd’s attention. Recently, a very active area of research has been to use humans as sensors in realworld observations that require a large amount of data. Some of these studies have attempted to produce real-time estimates of the subjects that are attracting a crowd’s attention by quickly collecting a large number of photographs. These studies are based on the assumption that, when photographers encounter interesting events, they take pictures. Some of the proposed methods realize high availability by using only photographing information, which includes information about location and azimuth of the camera and it is automatically embedded into photograph. Since this data is very small compared to that of the pixel information, the load on the communication infrastructure is reduced. However, there are problems with the accuracy when there are many attractive subjects in a small region, and they cannot be found with traditional methods that use a sequential search strategy. The proposed method overcomes this problem by applying nonnegative matrix factorization (NMF) to the estimated location of each subject. We verified the effectiveness of this by computational experiments and an experiment under a realistic environment.
Cite this article as:
Y. Kubo, M. Kubo, H. Sato, and A. Namatame, “Estimation of Locations of Densely Distributed Subjects Using NMF with Nonpixel Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.4, pp. 632-647, 2014.
Data files:
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