Long-Short Temporal–Spatial Clues Excited Network for Robust Person Re-identification

Li, Shuai; Song, Wenfeng; Fang, Zheng; Shi, Jiaying; Hao, Aimin; Zhao, Qi; Qin, Hong

doi:10.1007/s11263-020-01349-4

Cited by 9 publications

(1 citation statement)

References 82 publications

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“…In [113] long-short temporal spatial clues are used to obtain the robust representation of features. Proposed network does so by combining the motion appearance and motion refinement features.…”

Section: Cnn-based Approachesmentioning

confidence: 99%

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

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Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views. Owing to its potential in various applications and research significance, a plethora of deep learning based re-Id approaches have been proposed in the recent years. However, there exist several vision related challenges, e.g., occlusion, pose scale & viewpoint variance, background clutter, person misalignment and cross-domain generalization across camera modalities, which makes the problem of re-Id still far from being solved. Majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. In this context, a comprehensive review of current re-ID approaches in solving theses challenges is needed to analyze and focus on particular aspects for further advancements. At present, such a focused review does not exist and henceforth in this paper, we have presented a systematic challenge-specific literature survey of 230+ papers between the years of 2015-21. For the first time a survey of this type have been presented where the person re-Id approaches are reviewed in such solution-oriented perspective. Moreover, we have presented several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing person re-Id research and eventually help to develop practical real world solutions. Scope/Objective of the ReviewIn this paper, we have targeted the most popular challenges in person re-id to perform systematic challenge-wise review of the published approaches. In this context, we have collected papers from top conferences and journals for the years from 2015 to 2021. The progress in papers addressing each challenge and its influence on published results is

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Section: Cnn-based Approachesmentioning

confidence: 99%

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

View full text Add to dashboard Cite

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An efficient multi-scale channel attention network for person re-identification

Luo,

Shao,

Dang

et al. 2023

Vis Comput

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At present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient multi-scale channel attention network (EMCA) to learn robust and more discriminative features to solve these problems. Specifically, we designed a novel cross-channel attention module (CCAM) in EMCA and placed it after different layers in the backbone. The CCAM includes local cross-channel interaction (LCI) and channel weight integration (CWI). LCI focuses on both the maximum pooling features and the average pooling features to generate channel weights through convolutional layers, respectively. CWI combines the two channel weights to generate richer and more discriminant channel weights. Experiments on four popular person Re-ID datasets (Market-1501, DukeMTMC-ReID, CUHK-03 (detected) and MSMT17) show that the performance of our EMCA is consistently significantly superior to the existing state-of-the-art methods.

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Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification

Huang

et al. 2022

Int J Comput Vis

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Long-Short Temporal–Spatial Clues Excited Network for Robust Person Re-identification

Cited by 9 publications

References 82 publications

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

An efficient multi-scale channel attention network for person re-identification

Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification

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