基于跨模态近邻损失的可视-红外行人重识别

The goal of the visual-infrared person re-identification task is to search the image of a specific person in a given modality in the image set taken by other cameras in different modality to find out the corresponding image of the same person. Due to the different imaging methods, there are obvious modal differences between images of different modalities. Therefore, from the perspective of metric learning, the loss function is improved to obtain more discriminative information. The cohesiveness of image features is analyzed theoretically, and a re-recognition method based on cohesiveness analysis and cross-modal nearest neighbor loss function is proposed to strengthen the cohesiveness of different modal samples. The similarity measurement problem of cross-modal hard samples is transformed into the similarity measurement of cross-modal nearest neighbor sample pairs and the same modality sample pairs, which makes the optimization of modal cohesion of the network more efficient and stable. The proposed method is experimentally verified on the baseline networks of global feature representation and partial feature representation. Compared with the baseline method, the proposed method can improve the average accuracy of the visual and infrared person re-identification by up to 8.44%. The universality of the proposed method in different network architectures is proved. Moreover, at the cost of less model complexity and less computation, the reliable visual-infrared person re-identification results are achieved.