# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 1 Introduction  
Person re-identification (Re-ID) is a crucial task in computer vision with a wide range of applications, from security and public safety [1; 2] to retail analytics [3] and smart city implementations [4]. This task involves re-identifying individuals across different camera views by analyzing visual cues such as clothing, body shapes, and other attributes. Despite its significance, Re-ID faces substantial challenges, including variations in lighting, background clutter, and the presence of individuals with similar appearances. These factors reduce the effectiveness of Re-ID systems, particularly in dynamic environments where factors are uncontrollable. The primary challenge in Re-ID is to develop robust architectures capable of accurately matching a query image (\(\mathbf{q}\)) with the correct identity in a gallery set (\(\mathcal{G}=\{\mathbf{g}_{i}\mid i=1,2,\ldots,N\}\)). Recent trends have shifted towards video-based methodologies, leveraging datasets such as iLIDS-VID [5] and MARS [6]. These video sequences provide richer spatial and temporal details compared to static images. Advancements in deep learning have significantly improved Re-ID through the use of methods such as hypergraphs [7], attention mechanisms [8], pyramidal spatial-temporal aggregation [9], and vision transformers [10; 11]. However, these approaches primarily focus on appearance-based cues, leaving the challenge of similar appearances unresolved. To address these limitations, our research extends foundational studies by merging appearance-based features with gait features--a biometric modality essential for capturing distinctive walking patterns for individual identification. Traditionally, gait recognitionhas relied on silhouette-based methods [12] that focus on a person's shape and movement. However, recent advances in pose estimation have made skeletal-based gait analysis more feasible and effective. For instance, [PERSON] et al. [13] demonstrated highly accurate results using skeletal-based gait in controlled environments. Such advancements have now made skeletal-based approaches both viable and promising for Re-ID tasks. Building on these developments, we present an improved version of GAF-Net [14], which, to the best of our knowledge, is the first method to fuse skeletal-gait information with appearance-based data for comprehensive individual representation. Our paper refines this model by thoroughly exploring the original architecture and introducing advanced fusion techniques at both the feature and score levels. We investigate various approaches, including conditional concatenation and PCA-enhanced methods, to optimize the integration of appearance and gait features. We evaluate GAF-Net on both the iLIDS-VID and MARS datasets. The MARS dataset presents more challenging, real-world scenarios with shorter and more variable sequences, allowing us to assess GAF-Net's performance in diverse Re-ID contexts. This cross-dataset analysis provides insights into GAF-Net's effectiveness across different scenarios, ranging from long, informative sequences to challenging real-world conditions. While our results are promising, we also identify limitations, particularly in handling shorter, variable-length sequences. These findings guide our future research directions. Our research advances the field of algorithms and machine learning by introducing novel computational methods for video-based person re-identification. GAF-Net's innovative approach to feature extraction, fusion, and re-identification enhances the efficiency of multimodal data processing algorithms. This study provides valuable insights into algorithmic design for multimodal systems, addressing key challenges in computer vision and machine learning. By combining theoretical analysis with practical application, we offer a comprehensive framework for implementing advanced algorithms in this domain.