VSPeR: Self-Attention Based Hybrid Approach for Person Re-Identification in Realistic Environments

Person Re-Identification (ReID) remains a pivotal challenge in intelligent surveillance, constantly tested by dramatic intra-class variations, pose changes, lighting shifts, occlusions and distracting backgrounds. This paper proposes a self-attention based hybrid framework (VSPeR) designed to improve data diversity and visual quality for person re-identification in real-world scenarios. At the heart of VSPeR lies a powerful synergy: the robust, identity preserving feature extraction of Variational Autoencoders (VAEs) meets the precise, context-aware focus of Self-Attention Generative Adversarial Networks (GANs). This hybrid architecture captures rich, semantically meaningful latent representations while zeroing in on the most discriminative visual cues necessary for accurate cross-view matching. The outcome is high-fidelity, identity-consistent synthetic images that significantly amplify the training dataset enabling smarter, more effective learning. Put to the test across four leading ReID benchmarks, VSPeR achieves competitive performance compared to existing approaches in terms of mean Average Precision (mAP) and Rank-1 accuracy.