Human Attribute Recognition: A Comprehensive Survey

Yaghoubi, Ehsan; Khezeli, Farhad; Borza, Diana; Kumar, SV Aruna; Neves, João C.; Proença, Hugo

doi:10.20944/preprints202007.0055.v1

Cited by 2 publications

(1 citation statement)

References 113 publications

(176 reference statements)

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“…is also of practical significance in surveillance, which is imperative when the visual image of query person cannot be obtained. The re-id task now can be compiled as a human attribute recognition problem [161]. Kumar et al [71] has addressed this issue by collecting the aerial dataset, p-DESTRE, and annotating persons with 16 attributes.…”

Section: Open-world Aerial Person Re-idmentioning

confidence: 99%

The State of Aerial Surveillance: A Survey

Nguyen¹,

Fookes²,

Sridharan³

et al. 2022

Preprint

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The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs and other airborne platforms. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed. More specifically, for each of these four tasks, we first discuss unique challenges in performing these tasks in an aerial setting compared to a ground-based setting. We then review and analyze the aerial datasets publicly available for each task, and delve deep into the approaches in the aerial literature and investigate how they presently address the aerial challenges. We conclude the paper with discussion on the missing gaps and open research questions to inform future research avenues.

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Section: Open-world Aerial Person Re-idmentioning

confidence: 99%

The State of Aerial Surveillance: A Survey

Nguyen¹,

Fookes²,

Sridharan³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness

Pei

Fan

2022

Algorithms

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Existing person re-recognition (Re-ID) methods usually suffer from poor generalization capability and over-fitting problems caused by insufficient training samples. We find that high-level attributes, semantic information, and part-based local information alignment are useful for person Re-ID networks. In this study, we propose a person re-recognition network with part-based attribute-enhanced features. The model includes a multi-task learning module, local information alignment module, and global information learning module. The ResNet based on non-local and instance batch normalization (IBN) learns more discriminative feature representations. The multi-task module, local module, and global module are used in parallel for feature extraction. To better prevent over-fitting, the local information alignment module transforms pedestrian attitude alignment into local information alignment to assist in attribute recognition. Extensive experiments are carried out on the Market-1501 and DukeMTMC-reID datasets, whose results demonstrate that the effectiveness of the method is superior to most current algorithms.

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Human Attribute Recognition: A Comprehensive Survey

Cited by 2 publications

References 113 publications

The State of Aerial Surveillance: A Survey

The State of Aerial Surveillance: A Survey

Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness

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