Pedestrian re-identification domain generalization algorithm based on causal strong and weak alignment

Jianwen Mo; Junliang Hu; Hua Yuan; Leping Lin

doi:10.1117/12.3009544

16 October 2023 Pedestrian re-identification domain generalization algorithm based on causal strong and weak alignment

Jianwen Mo, Junliang Hu, Hua Yuan, Leping Lin

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030L (2023) https://doi.org/10.1117/12.3009544
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The domain generalisation pedestrian re-identification problem aims to generalise features learned in a known pedestrian data domain to an unknown pedestrian target domain. Most traditional domain generalization methods assume that the statistical properties between features and categories remain consistent across data domains. However, actual pedestrian data is often susceptible to domain factors such as lighting, colour shifts, and camera differences. This results in the data distribution of pedestrians under different domains not being identical and hinders the generalisation of the model. To address the above issues, this paper proposes a representation learning algorithm based on causal strong and weak alignment by constructing a structural causal model for the pedestrian domain generalization problem from the perspective of causal inference. The algorithm first performs causal intervention on the input pedestrian data to obtain causally enhanced images, then the image features are fed into the strong alignment module to achieve feature alignment in each dimension and obtain a preliminary invariant representation, finally, the features are then subjected to the constraints of the weak alignment module contrastive loss to further optimise the causal features under different cameras and improve the stability of the model’s cross-domain causal prediction. The method was compared and ablation experiments were carried out on the Market-1501 and DukeMTMC-reID datasets, demonstrating the effectiveness of the proposed method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianwen Mo, Junliang Hu, Hua Yuan, and Leping Lin "Pedestrian re-identification domain generalization algorithm based on causal strong and weak alignment", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030L (16 October 2023); https://doi.org/10.1117/12.3009544

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KEYWORDS

Data modeling

Education and training

Cameras

Alignment modeling

Ablation

Feature extraction

Fourier transforms

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