# A Person Re-Identification Method Based on Multi-Branch Feature Fusion
## 1 Introduction  
Person re-identification (Person re-ID) is of significant importance in video understanding. It aims to automatically identify pedestrian images in video images of different scenes based on a given picture of a target pedestrian. With the rapid development of deep learning, person re-ID methods based on deep learning are constantly emerging. These methods can extract advanced abstract features and effectively improve the accuracy of model recognition. The methods for extracting features from pedestrian images can be categorized into two distinct groups: global feature learning methods and local feature learning methods. Global feature learning methods usually extract features from pedestrian images [1; 2; 3], which makes it difficult to capture detailed information about pedestrians. Local feature-learning methods aim to learn the discriminative features of pedestrians and ensure the alignment of each local feature. Commonly, local feature learning methods include predefined stripe segmentation [4; 5], multi-scale fusion [6; 7; 8], soft attention [9; 10; 11], pedestrian semantic extraction [11; 12; 13], and global-local feature- learning [14; 15; 16]. These techniques can mitigate challenges, such as occlusions, errors in boundary detection, and variations in viewpoints and poses. Local feature learning methods excel at capturing intricate object details, yet their reliability can be compromised by variations in posture and occlusion. Consequently, prior studies have amalgamated fine-grained local features with grained global features to augment the representational capacity. [PERSON] et al. [17] proposed a multi-granularity feature learning strategy with global and local information, including one global feature learning branch to learn coarse-grained features and two local feature learning branches to learn fine-grained features. [PERSON] et al. [18] designed a Global-Local Dynamic Feature AlignmentNetwork (GLDFA-Net) framework with global and local branches and proposed a local sliding alignment method into the local branch to calculate the distance measure. However, the feature extraction networks of these models still lack specific designs for scenarios, such as scale changes [7; 19; 20], illumination differences [21; 22], and occlusions [23; 24; 25], so it is hard to apply them to real scenarios. In previous studies, particular attention was given to these common issues. [PERSON] et al. [25] proposed a novel Feature Erasing and Diffusion Network (FED), which incorporates an occlusion removal module trained to remove NPOs (Non-Pedestrian Occlusions) features based on predicted occlusion scores, guided by an image-level NPOs enhancement strategy. Subsequently, the feature diffusion module facilitates feature diffusion between the NPOs feature-erased pedestrian representation and memory features to synthesize Non-Target Pedestrian (NTP) features in the feature space. [PERSON] et al. [7] introduced a person re-ID method that, under a saliency model, enabling processing of images and extraction of salient area images of human bodies in pedestrian images to mitigate interference from complex backgrounds for person re-ID tasks. However, there is limited research addressing joint solutions to multiple typical problems. To address the aforementioned challenges, this paper proposes a Multi-Branch Feature Fusion Network (MFFNet) for addressing person re-ID problems in diverse scenarios. MFFNet comprises three branches: the multi-scale fusion global branch, the hybrid attention local branch, and the saliency guidance local branch. The global branch employs multi-scale feature fusion modules to balance the relationship between shallow image texture features and deep image semantic features, thereby enhancing the ability of the model to extract pedestrian features at various scales, scale changes, and illumination differences. The hybrid attention local branch utilizes HAM to focus on key information within spatial and channel features.