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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Recurrent Attention Model for Pedestrian Attribute Recognition

February 1, 2023

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Authors

Xin Zhao

Tsinghua University


Liufang Sang

Tsinghua University


Guiguang Ding

Tsinghua University


Jungong Han

Lancaster University


Na Di

Tsinghua University


Chenggang Yan

Hangzhou Dianzi University


DOI:

10.1609/aaai.v33i01.33019275


Abstract:

Pedestrian attribute recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that many semantic pedestrian attributes to be recognised tend to show spatial locality and semantic correlations by which they can be grouped while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature map, this paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other. RC model mines the correlations among different attribute groups with convolutional LSTM unit, while RA model takes advantage of the intra-group spatial locality and inter-group attention correlation to improve the performance of pedestrian attribute recognition. Our RA method combines the Recurrent Learning and Attention Model to highlight the spatial position on feature map and mine the attention correlations among different attribute groups to obtain more precise attention. Extensive empirical evidence shows that our recurrent model frameworks achieve state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.

Topics: AAAI

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HOW TO CITE:

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan Recurrent Attention Model for Pedestrian Attribute Recognition Proceedings of the AAAI Conference on Artificial Intelligence (2019) 9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan Recurrent Attention Model for Pedestrian Attribute Recognition AAAI 2019, 9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan (2019). Recurrent Attention Model for Pedestrian Attribute Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan. Recurrent Attention Model for Pedestrian Attribute Recognition. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan. 2019. Recurrent Attention Model for Pedestrian Attribute Recognition. "Proceedings of the AAAI Conference on Artificial Intelligence". 9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan. (2019) "Recurrent Attention Model for Pedestrian Attribute Recognition", Proceedings of the AAAI Conference on Artificial Intelligence, p.9275-9282

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan, "Recurrent Attention Model for Pedestrian Attribute Recognition", AAAI, p.9275-9282, 2019.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan. "Recurrent Attention Model for Pedestrian Attribute Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan. "Recurrent Attention Model for Pedestrian Attribute Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 9275-9282.

Xin Zhao||Liufang Sang||Guiguang Ding||Jungong Han||Na Di||Chenggang Yan. Recurrent Attention Model for Pedestrian Attribute Recognition. AAAI[Internet]. 2019[cited 2023]; 9275-9282.


ISSN: 2374-3468


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