# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 2 Related Works
### Gait Recognition  
As we shift focus from appearance-based Re-ID to biometric modalities, gait recognition emerges as a pivotal method in computer vision for identifying individuals. This approach relies on analyzing unique spatiotemporal patterns in a person's walking style, extracting distinct features that define an individual's gait.  
Gait recognition methodologies are categorized into appearance-based and model-based methods. Appearance-based approaches predominantly use silhouettes or body shapes for identification, as highlighted in [12; 23]. These methods can capture the visual elements of gait, providing a surface-level analysis.  
On the other hand, model-based approaches examine the structural aspects of the body, constructing two-dimensional (2D) or three-dimensional (3D) skeletal models, as demonstrated by [PERSON] et al. in [24]. Advances in pose estimation have significantly improved the accuracy of these methods.  
In the domain of gait recognition, these strategies further branch into different temporal representations, which are essential for capturing the dynamic nature of an individual's walk, such as image-based, sequence-based, and set-based techniques.  
In gait recognition, image-based methods, such as the Gait Entropy Images (GEI) approach proposed by [PERSON] et al. [25], aim to capture an entire gait cycle in a single image. While these methods are efficient, they can sometimes overlook important dynamic features of gait. To address this, sequence-based methods like the 3D-CNNs proposed by [PERSON] et al. [26] and the LSTMs proposed by [PERSON] et al. [24] focus on capturing the temporal evolution of gait, yet they require more computational resources. Set-based methods, as demonstrated in the work of [PERSON] et al. [27], blend various data inputs but do not explicitly model the temporal aspect, thereby achieving a balance between detail and computational efficiency. In this context, a study conducted by [PERSON] et al. [28] introduced an innovative approach for gait-based person Re-ID that combines signature extraction with matching techniques to analyze temporal gait elements. GaitPart, as presented by [PERSON] et al. [12], is a significant contribution to gait recognition, introducing a temporal module specifically designed to capture short-term temporal features within the gait cycle. This approach concentrates on subdividing the gait sequence into smaller, more manageable segments, facilitating a thorough examination of the gait pattern. This method provides deeper insights into the subtle variations in gait that occur over short intervals, rendering it a valuable tool for conducting more refined gait analysis.  
In model-based techniques, a recent development is the work of [PERSON] et al. [29], who proposed a novel self-supervised learning approach using 3D skeletal models for gait recognition. Traditional methods like CNNs, as shown in [30], and combinations of CNNs and LSTMs, as in [31], remain fundamental in the field. The use of graph convolutional networks, as explored by [PERSON] et al. in [13], represents an advanced technique for capturing human motion in gait analysis.  
It is important to note that while these methods are effective in controlled environments, such as those present in the CASIA-B dataset [32], their performance can vary significantly in more complex, real-world settings.