# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 4 Experimental Settings
### Datasets  
To rigorously assess GAF-Net's performance, we use two datasets, namely iLIDS-VID [5] and MARS [6]. Our primary focus is on iLIDS-VID due to its suitability for comprehensive gait analysis, while MARS serves to test our method's limitations in more complex, real-world scenarios.  
The iLIDS-VID dataset [5] is our primary evaluation benchmark, comprising 600 video sequences of 300 distinct pedestrians captured by two non-overlapping cameras in an airport setting. This dataset is particularly valuable for our research due to its long sequence lengths, ranging from 23 to 192 frames, with an average of 73 frames per sequence. These long sequences are crucial for capturing complete gait cycles, enabling a thorough evaluation of our gait-based features. iLIDS-VID presents significant challenges for person re-identification (Re-ID), including complex background clutter, lighting variations, and instances of self-occlusions and mutual occlusions. These characteristics make iLIDS-VID an ideal testbed for evaluating GAF-Net's ability to integrate appearance and gait features effectively in realistic scenarios.  
To further test GAF-Net's performance and find its potential weaknesses, we evaluate our method on the MARS (Motion Analysis and Re-identification Set) dataset [6]. MARS comprises over 20,000 video sequences collected from 1261 different individuals across 6 cameras installed on a university campus. Compared to iLIDS-VID, MARS is more diverse and complex. While some traklets contain between 25 and 50 frames, the majority of identities are represented by sequences with only 5 to 20 frames. This variation in sequence length adds to the dataset's complexity and poses a particular challenge for gait-based Re-ID methods, as many sequences may not contain a complete gait cycle. Additionally, MARS deliberately includes erroneous detection or tracking results as distractors. While these short tracklets are less ideal for comprehensive gait analysis, they reflect real challenges in practical video surveillance applications. The unique structure of MARS makes it a particularly demanding and representative benchmark for real-world re-identification scenarios, allowing us to test GAF-Net's performance under complex, real-world surveillance conditions and offering a rigorous evaluation framework for person re-identification algorithms.  
When considering datasets for person re-identification research, it is important to note that the PRID2011 dataset [46] offers limited challenges, with state-of-the-art methods achieving over 95% rank-1 accuracy [9]. Additionally, the DukeMTMC-VideoReIDdataset [47] was retracted due to ethical concerns. These factors underscore the importance of selecting datasets that are both technically challenging and ethically sourced for the evaluation of Re-ID algorithms.  
Our dual-dataset evaluation approach ensures a comprehensive evaluation of GAF-Net, leveraging iLIDS-VID's strengths for gait analysis while using MARS to assess robustness under challenging conditions. This strategy allows us to validate GAF-Net's capabilities across different real-world scenarios and provide insights into its potential and limitations in diverse Re-ID contexts.