# Cross-Domain Person Re-Identification Based on Feature Fusion Invariance
###### Abstract  
Cross-domain person re-identification is a technique for identifying the same individual across different cameras or environments that necessitates the overcoming of challenges posed by scene variations, which is a primary challenge in person re-identification and a bottleneck for its practical applications. In this paper, we learn the invariance model of cross-domain feature fusion in a labeled source domain and an unlabeled target domain. First, our method learns the global and local fusion features of a person in the source domain by means of supervised learning with no component label and only person identification and obtains the fusion features of the person in the source and target domains by means of unsupervised learning. Based on person fusion features, this paper introduces feature memory to store the fused target features and designs a cross-domain invariance loss function to improve the cross-domain adaptability of the person. Finally, this paper carries out cross-domain person re-identification verification experiments between the Market-1501 and DukeMTMC-reID datasets; the experimental results show that the proposed method achieves significant performance improvement in cross-domain person re-identification. person re-identification; feature fusion; feature memory; cross-domain invariance +
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jointly visible in two person images to solve the spatial location mismatch between person parts and the overall person image, which significantly improves the feature representation learning ability. [PERSON] et al. [6] proposed a part convolution pooling method based on person local features to enhance the semantic consistency of person part regions in person re-identification. The introduction of a deep learning-based attention mechanism [7] to a person rerecognition model to automatically assign a weight to each person image further boosts person rerecognition performance. [PERSON] et al. [8] proposed a person re-identification model based on a dual attention mechanism through CNN feature channel aggregation and spatial location awareness, which integrates an attention module and diversity regularization to learn representative, robust, and more discriminative features. [PERSON] et al. [9] propose a Diverse and Compact Transformer (DC-Former) that achieves a similar effect by partitioning the embedding space into multiple diverse and compact subspaces. The compact embedding subspace facilitates the model in learning more robust and discriminative embeddings for identifying similar classes, while the fusion of these diverse embeddings containing finer-grained information further enhances the effectiveness of reID. [PERSON] et al. [10] propose a generic Transformer-based Skeleton Graph prototype contrastive learning (TranSG) approach with structure-trajectory prompted reconstruction to comprehensively capture skeletal relations and valuable spatial-temporal semantics from skeleton graphs for person re-identification. Although the above approach considers local instance knowledge, it fails to achieve simultaneous feature discovery for samples at both the global distribution and local instance levels. The above supervised learning-based person re-identification methods rely on rich labeled data and deep networks to achieve success on single-domain datasets. However, serious performance degradation occurs when this approach is applied to unknown datasets. In realistic surveillance scenarios, collecting and labeling a large quantity of training data for a specific scene is not feasible. Therefore, unsupervised domain adaptation (UDA) methods have become a popular research topic [11]. UDA methods are mainly classified into image-integration, feature distribution, and clustering-based adaptation methods.