Normalized Metric Learning Based on Multi-feature Fusion for Person Re-identification

Abstract

Person re-identification remains a critical conundrum of intelligent video surveillance system, which intends to query pedestrians with the same identity. It is of great significance to extract features with sharp discrimination and robustness, especially for images captured in complex scenes, such as illumination variance, different pose and viewpoints. In this paper, we propose a normalized distance aggregation strategy, which integrates three discriminative pedestrian feature extraction models, i.e., low-level local maximal occurrence (LOMO), mid-level salient color names based color descriptor (SCNCD), and high-level feature fusion net (FFN). We utilize each feature descriptor to train two distance metric learning models, including large-scale similarity learning (LSSL) and cross-view quadratic discriminant analysis (XQDA), respectively. Accordingly, the ultimate distance is calculated as the sum of six optimized distance metrics by the min-max normalization with respective weights. In experiments, comprehensive evaluation results indicate that our framework gains superior performance with robustness to illumination, viewpoint as well as pose changes, and exceeds the existing methods on the public dataset VIPeR.