## 4 Experiments and Results
### Implementation Details  
Experiments were conducted on a desktop PC with Windows 11, WSL2 (Ubuntu 22.04 LTS), a 24 GB GPU, 32 GB RAM, and 1 TB SSD. We fine-tuned YOLOv8 for person detection across the selected datasets. Each dataset was converted to a YOLOv8-compatible format, retaining only the person class, and organized into training and validation directories with corresponding annotation files.  
The fine-tuning utilized a custom Python training script with the PyTorch framework [59] and the Ultralytics YOLOv8 library, configured for 100 epochs, 640 \(\times\) 640 pixel images, and a batch size of 16. Data augmentation techniques, including multi-scale training and horizontal flipping, were applied to enhance model robustness.  
For the VLM module of ReTrackVLM, we developed a data processing pipeline to convert datasets for Florence-2, CLIP, and DETR2. This pipeline processes annotations by integrating bbx information, image dimensions, keypoints, descriptors, overlap ratios, and confidence scores into a unified format. The bbx coordinates and image size are extracted directly from YOLOv8 outputs, ensuring accurate localization of objects within each frame. Keypoints and descriptors, which provide additional semantic and structural details such as image-level metadata (e.g., image dimensions), are derived using XFeat and LightGLUE, respectively. These features are then normalized to ensure consistency across frames and datasets.  
The structured embedding representation leverages a fixed format that includes image-specific information (e.g., dimensions and bbxes) along with semantic details (e.g., keypoints and descriptors). This fixed structure not only ensures compatibility across modules but also significantly improves capacity management and computational efficiency. Keypoints represent specific positions on detected objects, such as joints or other salient features, with each keypoint described by two coordinates indicating its position. To optimize memory usage, we limited the number of keypoints based on their confidence scores, retaining only the highest confidence points. Descriptors, extracted alongside keypoints, are 64-dimensional vectors describing the local image regions around each keypoint. These descriptors provide rich contextual information for fine-grained identification and tracking. By standardizing the embedding structure, the framework effectively reduces memory usage and enhances processing speed, enabling its deployment in resource-constrained environments. This efficient embedding organization was pivotal in achieving timely performance without compromising accuracy, particularly for tasks involving dense or complex scenes. This compact format enables efficient storage and processing while ensuring the semantic and spatial integrity of the data. The combination of YOLOv8, XFeat, and LightGLUE provides a robust foundation for generating embeddings that are both computationally efficient and effective for ReID and tracking tasks. These optimizations support scalability and practical applicability, making the ReTrackVLM framework suitable for both research and real-world scenarios.