Stronger Heterogeneous Feature Learning for Visible-Infrared Person Re-Identification

Abstract

Visible-Infrared person re-identification (VI-ReID) is of great importance in the field of intelligent surveillance. It enables re-identification of pedestrians between daytime and dark scenarios, which can help police find escaped criminals at night. Currently, existing methods suffer from inadequate utilisation of cross-modality information, missing modality-specific discriminative information and weaknesses in perceiving differences between different modalities. To solve the above problems, we innovatively propose a stronger heterogeneous feature learning (SHFL) method for VI-ReID. First, we innovatively propose a Cross-Modality Group-wise constraint to solve the problem of inadequate utilization of cross-modality information. Secondly, we innovatively propose a Second-Order Homogeneous Invariant Regularizer to address the problem that missing modality-specific discriminative information. Finally, we innovatively propose a Modality-Aware Batch Normalization to address the problem of weaknesses in perceiving differences between different modalities. Extensive experimental results on two generic VI-ReID datasets demonstrate that the proposed final method outperforms the state-of-the-art algorithms.