Improve Person Re-Identification With Part Awareness Learning

Huang, Houjing; Yang, Wenjie; Lin, Jinbin; Huang, Guan; Xu, Jiamiao; Wang, Guoli; Chen, Xiaotang; Huang, Kaiqi

doi:10.1109/tip.2020.3003442

Cited by 22 publications

(4 citation statements)

References 54 publications

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“…To be specific, the image is divided into multiple horizontal stripes, and the convolution neural network is applied on different stripes to extract the local features of different regions. Therefore, there are many image segmentation strategies [13][14][15][16].…”

Section: Part-based Methodsmentioning

confidence: 99%

Person Re‐identification Based on Feature Erasure and Diverse Feature Learning

Jidong

Zhang

et al. 2022

IET Computer Vision

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Robust and comprehensive modelling of targets is the key to successful person re‐identification. However, some useful information may be ignored, since CNNs tend to learn from the most distinctive feature region of the human body. In the present study, a multi‐branch lightweight network structure that can enhance the ability of diverse feature retrieval is introduced. The proposed network consists of three branches. In the feature erasure branch, a drop block model is added to remove the horizontal region with the highest activation degree from feature vectors so as to allow the network to learn relatively low discrimination features. The global branch is used as an essential supplement to the feature erasure branch. A unified horizontal segmentation strategy is adopted in the local branch to avoid the influence of feature dislocation. Finally, diverse feature learning is achieved through the branch network structure. The proposed method can achieve state‐of‐the‐art results on Market‐1501, CUHK03 and DukeMTMC‐Reid data sets, thereby demonstrating the effectiveness of the method.

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Section: Part-based Methodsmentioning

confidence: 99%

Person Re‐identification Based on Feature Erasure and Diverse Feature Learning

Jidong

Zhang

et al. 2022

IET Computer Vision

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“…Another approach to mitigate the challenges i.e. viewpoint variation, low resolution and pose was presented in [189]. A lightweight and labelled part segmentation head was added to the backbone of re-id during training process to obtain diverse set of features hence resulted in improved performance of re-id.…”

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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“…Exploring similarity correctly is critical to the re-ID performance. Supervised person re-ID methods can use additional human pose labels [6] or human body part segmentation regions [7] to help model learn human part similarity. However, these pre-annotated auxiliary labels are unknowable in the unsupervised learning task.…”

Section: Similarity Exploration For Unsupervised Re-idmentioning

confidence: 99%