Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning

Author:

Zhao Xin1,Sang Liufang1,Ding Guiguang1,Guo Yuchen1,Jin Xiaoming1

Affiliation:

1. Tsinghua National Laboratory for Information Science and Technology (TNList), School of Software, Tsinghua University, Beijing 100084, China

Abstract

Pedestrian attributes 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 semantic pedestrian attributes to be recognised tend to show semantic or visual spatial correlation. Attributes can be grouped by the correlation while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)'s super capability of learning context correlations, this paper proposes an end-to-end Grouping Recurrent Learning (GRL) model that takes advantage of the intra-group mutual exclusion and inter-group correlation to improve the performance of pedestrian attribute recognition. Our GRL method starts with the detection of precise body region via Body Region Proposal followed by feature extraction from detected regions. These features, along with the semantic groups, are fed into RNN for recurrent grouping attribute recognition, where intra group correlations can be learned. Extensive empirical evidence shows that our GRL model achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Orientation-Aware Pedestrian Attribute Recognition Based on Graph Convolution Network;IEEE Transactions on Multimedia;2024

2. PARFormer: Transformer-Based Multi-Task Network for Pedestrian Attribute Recognition;IEEE Transactions on Circuits and Systems for Video Technology;2024-01

3. POAR: Towards Open Vocabulary Pedestrian Attribute Recognition;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26

4. Multi-Task Collaborative Attention Network for Pedestrian Attribute Recognition;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

5. Global-guided weakly-supervised learning for multi-label image classification;Journal of Visual Communication and Image Representation;2023-05

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