Attribute-related graph visual dependencies for pedestrian attribute recognition

Xinyu Song; Wenrui Ding; Yufeng Wang; Yalong Jiang

doi:10.1117/12.2681033

8 June 2023 Attribute-related graph visual dependencies for pedestrian attribute recognition

Xinyu Song, Wenrui Ding, Yufeng Wang, Yalong Jiang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270740 (2023) https://doi.org/10.1117/12.2681033
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Pedestrian attribute recognition has attracted extensive attention in surveillance scenarios. Many researchers treat it as a multi-label classification problem. However, they ignore the relations among attributes. For example, long hair and female often appear together at the semantic level, and there exists an overlap between their attribute-related regions at the visual level, e.g., female-related regions including long-hair-related head area and skirt-related areas. There is a correspondence between the semantic level and the visual level. However, existing relation-based methods which explore relations among attributes mostly ignore the correspondence between semantic and visual levels in relations among attributes. Our main contribution lies in proposing a novel end-to-end trainable Attribute-related Graph Visual Dependencies (AGVD) framework that can learn the dependencies of attributes based on extracted attribute-related visual features. Moreover, we propose a Graph Fusion mechanism to guide the attribute-related graph generation and pruning via semantic relations innovatively. We conduct experiments on three large-scale pedestrian attribute datasets. The experiments on PETA, RAP, and PA-100k have demonstrated that the AGVD outperforms the previous state-of-the-art methods.

Citation Download Citation

Xinyu Song, Wenrui Ding, Yufeng Wang, and Yalong Jiang "Attribute-related graph visual dependencies for pedestrian attribute recognition", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270740 (8 June 2023); https://doi.org/10.1117/12.2681033

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KEYWORDS

Visualization

Semantics

Feature extraction

Visual process modeling

Education and training

Mining

Binary data

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