Efficient Deep Learning Approach to Address Low-Resolution Person Re-Identification
Authors: 
Irfan Yaqoob, Muhammad Umair Hassan, Dongmei Niu, Muhammad Maaz Irfan, Numan Zafar, Xiuyang ZhaoAuthors Info & Claims
Article No.: 22, Pages 1 - 5
Abstract
Person re-identification is a vital part of the computer vision and plays an important role, especially for surveillance applications, e.g., intelligent video analysis, forensic, behavior analysis, and robotics. Mostly it is assumed that gallery and target images have the same resolution. However, this assumption fails in real-world re-identification because images taken by security cameras are mostly low-resolution and have pose variations, background clutters, and occlusions. So it makes the re-identification a challenging task. We proposed an efficient deep learning approach to address the low-resolution problem that regenerates low-resolution images to uniform high-resolution images. Afterward, these high-resolution images are passed to the re-identification network to get the final output. We employ a feedback network to convert low-resolution images to high-resolution. Our network's performance is tested on two datasets, MLR-VIPeR, MLR-DukeMTMC-ReID, and our model achieved superior results compared to the other algorithms.
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Index Terms
- Efficient Deep Learning Approach to Address Low-Resolution Person Re-Identification
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Published In
December 2020
366 pages
ISBN:9781450389532
DOI:10.1145/3448823
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Published: 04 March 2021
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ICVISP 2020
ICVISP 2020: 2020 4th International Conference on Vision, Image and Signal Processing
December 9 - 11, 2020
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