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Low illumination person re-identification.

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Title: Low illumination person re-identification.
Authors: Ma, Fei1, Zhang, Xinyu1, Yang, Liang1, Zuo, Mei1, Jing, Xiao-Yuan1, Zhu, Xiaoke2
Source: Multimedia Tools & Applications. Jan2019, Vol. 78 Issue 1, p337-362. 26p.
Subjects: Lighting, Tracking & trailing, Videos, Spacetime, Photographs
Abstract: Low illumination is a common problem for recognition and tracking. Low illumination video-based person re identification (re-id) is an important application in practice. Low illumination usually results in severe loss of visual appearance and space-time information contained in pedestrian image or video, which brings large difficulty to re-identification. However, the problem of low illumination video-based person re-id (LIVPR) has not been well studied. In this paper, we propose a novel triplet-based manifold discriminative distance learning (TMD2L) approach for LIVPR. By regarding each video as an image set, TMD2L aims to learn a manifold-based distance metric, under which the intrinsic structure of image sets can be preserved, and the distance between truly matching sets is smaller than that between wrong matching sets. Experiment results on the new collected low illumination person sequence (LIPS) dataset, as well as two simulated datasets LI-PRID 2011 and LI-iLIDS-VID show that our proposed approach TMD2L outperforms existing representative person re-id methods. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Low illumination is a common problem for recognition and tracking. Low illumination video-based person re identification (re-id) is an important application in practice. Low illumination usually results in severe loss of visual appearance and space-time information contained in pedestrian image or video, which brings large difficulty to re-identification. However, the problem of low illumination video-based person re-id (LIVPR) has not been well studied. In this paper, we propose a novel triplet-based manifold discriminative distance learning (TMD2L) approach for LIVPR. By regarding each video as an image set, TMD2L aims to learn a manifold-based distance metric, under which the intrinsic structure of image sets can be preserved, and the distance between truly matching sets is smaller than that between wrong matching sets. Experiment results on the new collected low illumination person sequence (LIPS) dataset, as well as two simulated datasets LI-PRID 2011 and LI-iLIDS-VID show that our proposed approach TMD2L outperforms existing representative person re-id methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-018-6239-3
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      – Code: eng
        Text: English
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              Text: Jan2019
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