Soft-Attention Based Person Re-Identification in Real-world Settings using Variational AutoEncoders

Person re-identification is still an open challenging task in various fields due to numerous factors, including illumination changes, background clutter, pose state variations and cloth changes. Several approaches have been suggested to address this problem in the context of deep learning. Generative models, particularly Variational Autoencoders (VAEs), have emerged as promising tools to address these challenges by learning discriminative feature representations of individual images. In this paper, we present Soft-Attention based Person Re-Identification (SAPRI), a novel approach that combines VAEs with a supervised ReID method to enhance the resilience and efficacy of ReID systems. The proposed approach focuses on data reconstruction based on soft attention. Variational autoen-coders encode principally person data, while ignoring irrelevant information. By incorporating supervised ReID, the model learns to appropriately classify persons in real world environments. Our SAPRI proposed method has been evaluated on well-known benchmarks, DukeMTMC-reID and CUHK03, demonstrating superior performance compared to existing state-of-the-art techniques in terms of the mean Average Precision evaluation metric (mAP). Additionally, qualitative results show the effectiveness of the VAE in generating discriminative representations of person images.