Reidentification of Persons Using Clothing Features in Real-Life Video
Joint Authors
Matsumoto, Kazuyuki
Zhang, Guodong
Jiang, Peilin
Yoshida, Minoru
Kita, Kenji
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing.
Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties.
The color histogram is a global feature of an object that can be used for identification.
This histogram describes the distribution of all colors on the object.
However, the use of color histograms has two disadvantages.
First, colors change differently under different lighting and at different angles.
Second, traditional color histograms lack spatial information.
We used a perception-based color space to solve the illumination problem of traditional histograms.
We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms.
Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes.
Through a series of experiments, we found the relationships of different features that impact person reidentification.
American Psychological Association (APA)
Zhang, Guodong& Jiang, Peilin& Matsumoto, Kazuyuki& Yoshida, Minoru& Kita, Kenji. 2017. Reidentification of Persons Using Clothing Features in Real-Life Video. Applied Computational Intelligence and Soft Computing،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1121439
Modern Language Association (MLA)
Zhang, Guodong…[et al.]. Reidentification of Persons Using Clothing Features in Real-Life Video. Applied Computational Intelligence and Soft Computing No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1121439
American Medical Association (AMA)
Zhang, Guodong& Jiang, Peilin& Matsumoto, Kazuyuki& Yoshida, Minoru& Kita, Kenji. Reidentification of Persons Using Clothing Features in Real-Life Video. Applied Computational Intelligence and Soft Computing. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1121439
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121439