Attribute-aware pedestrian detection in a crowd
Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to hea...
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sg-smu-ink.sis_research-79702022-03-04T05:50:49Z Attribute-aware pedestrian detection in a crowd ZHANG, Jialiang LIN, Lixiang ZHU, Jianke LI, Yang CHEN, Yun-chen HU, Yao HOI, Steven C. H. Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression (NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, an enhanced ground truth target is designed to alleviate the difficulties caused by the attribute configuration, and to ease the class imbalance issue during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on three benchmark datasets including CityPerson, CrowdHuman, and EuroCityPerson, and achieves the state-of-the-art results. 2021-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6967 info:doi/10.1109/TMM.2020.3020691 https://ink.library.smu.edu.sg/context/sis_research/article/7970/viewcontent/AttributeAwarePedestrian_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Attribute-aware non-maximum suppression (nms) pedestrian detection Databases and Information Systems Numerical Analysis and Scientific Computing |
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Attribute-aware non-maximum suppression (nms) pedestrian detection Databases and Information Systems Numerical Analysis and Scientific Computing |
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Attribute-aware non-maximum suppression (nms) pedestrian detection Databases and Information Systems Numerical Analysis and Scientific Computing ZHANG, Jialiang LIN, Lixiang ZHU, Jianke LI, Yang CHEN, Yun-chen HU, Yao HOI, Steven C. H. Attribute-aware pedestrian detection in a crowd |
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Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression (NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, an enhanced ground truth target is designed to alleviate the difficulties caused by the attribute configuration, and to ease the class imbalance issue during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on three benchmark datasets including CityPerson, CrowdHuman, and EuroCityPerson, and achieves the state-of-the-art results. |
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author |
ZHANG, Jialiang LIN, Lixiang ZHU, Jianke LI, Yang CHEN, Yun-chen HU, Yao HOI, Steven C. H. |
author_facet |
ZHANG, Jialiang LIN, Lixiang ZHU, Jianke LI, Yang CHEN, Yun-chen HU, Yao HOI, Steven C. H. |
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ZHANG, Jialiang |
title |
Attribute-aware pedestrian detection in a crowd |
title_short |
Attribute-aware pedestrian detection in a crowd |
title_full |
Attribute-aware pedestrian detection in a crowd |
title_fullStr |
Attribute-aware pedestrian detection in a crowd |
title_full_unstemmed |
Attribute-aware pedestrian detection in a crowd |
title_sort |
attribute-aware pedestrian detection in a crowd |
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Institutional Knowledge at Singapore Management University |
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2021 |
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https://ink.library.smu.edu.sg/sis_research/6967 https://ink.library.smu.edu.sg/context/sis_research/article/7970/viewcontent/AttributeAwarePedestrian_av.pdf |
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