Bayesian classification algorithm in the person re-identification task

Purpose of research. Development and experimental evaluation of a Bayesian classification algorithm for the person re-identification task using images from multiple surveillance cameras. The study aims to improve identification accuracy through integrating features derived from facial and silhouette images. Methods. The proposed algorithm utilizes a Bayesian classification model based on multivariate normal distributions of features. These features are extracted by neural encoders built on the Vision Transformer architecture and trained using the ArcFace loss function. Integration of modality-specific features is performed by computing logarithmic posterior probabilities of class membership. The effectiveness of the method was evaluated using the open CUHK03 dataset, quantitative analysis via ROC curves, and feature space visualization using the t-SNE method. Results. The algorithm demonstrated high classification performance: precision of 95.65% on CUHK03, up to 97.7% on Market-1501, and 89.2% on MARS. ROC analysis confirmed strong class separability, while t-SNE visualizations showed compact and well-defined clusters. The algorithm is deterministic, robust to noise, and scalable to larger datasets. Conclusion. The developed Bayesian classification algorithm has proven its effectiveness and feasibility for person re-identification tasks in intelligent video surveillance systems. Its advantages include high accuracy, interpretability, and potential for integrating additional features. Future research should focus on incorporating extra attributes and evaluating algorithm performance on significantly larger and more diverse datasets..