Incremental Generative Occlusion Adversarial Suppression Network for Person ReID

Zhao, Cairong; Lv, Xinbi; Dou, Shuguang; Zhang, Shanshan; Wu, Jun; Wang, Liang

doi:10.1109/tip.2021.3070182

Cited by 65 publications

(22 citation statements)

References 38 publications

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“…The person reidentification model is fundamentally a data-driven deep learning model, and the diversity of training data plays a crucial role in its generalization and robustness ( Huang et al, 2021 ). To enhance the robustness and generalization of the trained model, unlike existing person reidentification methods ( Ning et al, 2020 , Zhao et al, 2021 ), which typically assess the model’s performance on several datasets separately, this study employs the fusion of multiple datasets as the training set when constructing the model for contact tracking. Furthermore, the test data was collected from a real office building to evaluate the performance of the proposed contact tracking method as a typical case.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 contact tracking based on person reidentification and geospatial data

Zhang

Lei

Cai

et al. 2023

Journal of King Saud University - Computer and Information Scie

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Section: Methodsmentioning

confidence: 99%

COVID-19 contact tracking based on person reidentification and geospatial data

Zhang

Lei

Cai

et al. 2023

Journal of King Saud University - Computer and Information Scie

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“…Cairong Zhao et al in [61] presented an Incremental Generative Occlusion Adversarial Suppression (IGOAS) network. The proposed model consists of two blocks i.e.…”

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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“…Recently, with the exploration of attention mechanisms for various vision tasks, it has also been adopted for occluded person Re-ID to eliminate the interference of noisy information [44,29,12]. During the process of attention learning, many data augmentation strategies [1,36,51] generate artificial occlusion, which directs the attention to person and forces it to avoid occluded regions.…”

Section: Introductionmentioning

confidence: 99%

Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-Identification

Xia¹,

Tan²,

Dai³

et al. 2023

Preprint

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Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the network. To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion. Secondly, to fully exploit these complex occluded images, we develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dualpath interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.

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Incremental Generative Occlusion Adversarial Suppression Network for Person ReID

Cited by 65 publications

References 38 publications

COVID-19 contact tracking based on person reidentification and geospatial data

COVID-19 contact tracking based on person reidentification and geospatial data

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

Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-Identification

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