2020 25th International Conference on Pattern Recognition (ICPR) 2021
DOI: 10.1109/icpr48806.2021.9413228
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Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification

Abstract: Video-based person re-identification has received increasing attention recently, as it plays an important role within surveillance video analysis. Video-based Re-ID is an expansion of earlier image-based re-identification methods by learning features from a video via multiple image frames for each person. Most contemporary video Re-ID methods utilise complex CNNbased network architectures using 3D convolution or multibranch networks to extract spatial-temporal video features. By contrast, in this paper, we ill… Show more

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Cited by 4 publications
(2 citation statements)
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References 37 publications
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“…With the development of deep learning, many areas have gained great success including person re-identification. At first, most works extract features for each frame by CNN backbones such as ResNet-50 and use global average pooling or some simple weighted method to fuse them like Zhao et al ( 2019 ); Breckon and Alsehaim ( 2021 ) and Zheng et al ( 2016 ). It helps them to achieve significant results, but they merely use temporal clues to build up a discriminative representation.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…With the development of deep learning, many areas have gained great success including person re-identification. At first, most works extract features for each frame by CNN backbones such as ResNet-50 and use global average pooling or some simple weighted method to fuse them like Zhao et al ( 2019 ); Breckon and Alsehaim ( 2021 ) and Zheng et al ( 2016 ). It helps them to achieve significant results, but they merely use temporal clues to build up a discriminative representation.…”
Section: Related Workmentioning
confidence: 99%
“…The development of personal intelligence, such as home robotics technology, also needs the support of Re-ID technologies. Most relative works (Liu et al, 2015;Xu et al, 2017;Breckon and Alsehaim, 2021) can be summarized as two main steps: spatial feature extraction and temporal feature aggregation. CNN will be used as a spatial feature extractor for each frame, and the generated frame-level features will be sent to the aggregation part to produce a video-level feature.…”
Section: Introductionmentioning
confidence: 99%