Improved Fuzzified LA Transformer Integrated CNN for Person Reidentification

Person re-identification is one of the challenging tasks in computer vision where the aim is to identify individuals accurately across various camera views in complex environments. Here, an advanced framework combining LA Transformer models and Convolutional Neural Networks with robust feature extraction for CNN and LA Transformer models with part-based attention mechanisms which are meant to deal with occlusion and appearance variation and aimed at extracting local features. The features extracted by the approach above are fused and refined with a Fuzzy Neural Network to integrate using fuzzy logic better features and improve classification accuracy. The proposed approach is effective due to the experimental result, which even surpasses standard approaches in challenging instances with higher Rank-1 accuracy and Mean Average Precision (mAP) for person re-identification benchmark datasets.