## 2 Related Works
The online multi-object tracker in [34] combines various appearance features with a ReID network to reduce IDSws, while our method leverages visual language models (VLMs) and advanced track management for superior IDSw and occlusion handling. The denoising diffusion strategy in [35] enhances tracking by jointly detecting and associating objects, yet ReTrackVLM offers a more comprehensive solution with its track management module and zero-shot ReID system. The approach in [36] uses weak cues alongside strong spatial and appearance information, whereas our method ensures better generalization through hybrid VLMs and detailed embeddings. The SMILETrack method in [37] combines an efficient detector with a Siamese network for similarity learning; our approach surpasses it with a more advanced embedding structure and track management module. AMDDATrack in [38] is designed to improve tracking accuracy by addressing trajectory breaks caused by dropped low-scoring detection frames utilizing an improved CenterNet [39] detection network incorporating a feature pyramid network, a high-resolution feature map, and a spatial attention mechanism to enhance detection accuracy. Similarly, REACTrack, proposed in [40], also employs CenterNet, but with a focus on enhancing ReID robustness and correcting tracking association errors, especially in complex scenarios like occlusion. The work in [41] integrates temporal and spatial features using a Kalman filter and Hungarian algorithm for tracking in self-driving cars, but struggles with similar-looking or closely blocked objects. Fast re-OBJ [42] improves performance by tightly coupling instance segmentation and embedding generation for a more discriminative representation. The sports-focused Deep-EIoU method [43] replaces the Kalman filter with an iterative scale-up approach, while our work broadens applicability through fine-tuning on diverse datasets and a novel ReID module. The MG-MOT algorithm in [44] integrates UAV metadata for maritime ReID, demonstrating strong performance, but our approach generalizes better across various environments. ReTrackVLM distinguishes itself by integrating VLMs and a novel embedding structure specifically designed for MOT in complex environments characterized by severe occlusions. Recent research supports the effectiveness of this approach, as shown in [45; 46], which introduces robust MOT methods for sports scenarios and real-time solutions, respectively, emphasizing the importance of embedding strategies and track management central to our methodology. Additionally, methods like OR-SORT [47] and OneTracker [48] address challenges related to camera motion and multi-modality through a Foundation Tracker, highlighting the potential of VLMs and advanced embeddings in MOT. Similarly, refs. [49; 50] propose innovative MOT paradigms incorporating natural language descriptions and multi-modality tracking. Our method builds upon these advancements by refining embedding and track management techniques specifically tailored for human tracking across diverse datasets. Furthermore, the perspective disentanglement framework for ReID in [51] aligns with our focus on robust embedding strategies for track management. Cross-domain ReID methods [52] highlight ongoing efforts to improve domain adaptation in MOT. While other MOT methods have incorporated VLMs, ReTrackVLM distinguishes itself through its integration of zero-shot ReID for track association and its enhanced track management module. Unlike existing methods that rely solely on fine-tuned embeddings or pre-trained models, ReTrackVLM effectively utilizes cross-modal embeddings to improve robustness in challenging scenarios, such as occlusion and crowded scenes. The novelty lies in its ability to seamlessly integrate these components into a unified framework, which is validated across diverse datasets.