Reidentification of Persons Using Clothing Features in Real-Life Video
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 co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2017/5834846 |
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| author | Guodong Zhang Peilin Jiang Kazuyuki Matsumoto Minoru Yoshida Kenji Kita |
| author_facet | Guodong Zhang Peilin Jiang Kazuyuki Matsumoto Minoru Yoshida Kenji Kita |
| author_sort | Guodong Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-445dd591b1ff48b9a6cebbe82e0566af |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-445dd591b1ff48b9a6cebbe82e0566af2025-02-03T00:59:44ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322017-01-01201710.1155/2017/58348465834846Reidentification of Persons Using Clothing Features in Real-Life VideoGuodong Zhang0Peilin Jiang1Kazuyuki Matsumoto2Minoru Yoshida3Kenji Kita4Faculty of Engineering, Tokushima University, Tokushima 7708506, JapanXian Jiao Tong University, No. 28, Xianning West Road, Xian, ChinaFaculty of Engineering, Tokushima University, Tokushima 7708506, JapanFaculty of Engineering, Tokushima University, Tokushima 7708506, JapanFaculty of Engineering, Tokushima University, Tokushima 7708506, JapanPerson 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.http://dx.doi.org/10.1155/2017/5834846 |
| spellingShingle | Guodong Zhang Peilin Jiang Kazuyuki Matsumoto Minoru Yoshida Kenji Kita Reidentification of Persons Using Clothing Features in Real-Life Video Applied Computational Intelligence and Soft Computing |
| title | Reidentification of Persons Using Clothing Features in Real-Life Video |
| title_full | Reidentification of Persons Using Clothing Features in Real-Life Video |
| title_fullStr | Reidentification of Persons Using Clothing Features in Real-Life Video |
| title_full_unstemmed | Reidentification of Persons Using Clothing Features in Real-Life Video |
| title_short | Reidentification of Persons Using Clothing Features in Real-Life Video |
| title_sort | reidentification of persons using clothing features in real life video |
| url | http://dx.doi.org/10.1155/2017/5834846 |
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