## 2 Related Works  
Object occlusion poses a significant challenge in multi-object tracking (MOT), often causing mismatches when occluded objects reappear. Consistently maintaining object IDs, even amidst varying occlusion scenarios, is essential for tracking accuracy. Many solutions have been proposed to enhance robustness and accuracy in MOT, with our methodology integrating advanced techniques targeting occlusion complexities. Key challenges include the following: _(i)_ Temporary object occlusions disrupt the tracking process, making it difficult to maintain accurate tracks as objects obscure each other. _(ii)_ Changes in illumination, pose, or scale result in significant appearance variations, requiring robust algorithms capable of adapting to these changes. _(iii)_ Real-time processing is essential, as tracking multiple objects in high-resolution video streams demands computational efficiency. _(iv)_ In dense environments with closely interacting objects, maintaining track identity becomes particularly challenging. To address these, various approaches have emerged. For example, a dynamic representation-based tracker [11] uses an adaptive representation network and pose supervision for long-term tracking with occlusions. The SiamFEA tracker [12] combines visible and infrared modalities using self-attention mechanisms. iReIDNet [13] enhances person ReID through spatial feature transforms and coordinate attention. A transformer-based dual-branch model [14] improves performance via global-local feature interaction, while contextual relation networks [15] tackle similar local feature issues. Advancements also include deep convolutional architectures [16], color descriptor-based ReID methods [17], hierarchical clustering frameworks [18], graph-based approaches [19], and recurrent neural networks [20] to enhance data association and occlusion handling. Recent work on feature embedding aims to reduce dimensionality while retaining key characteristics, with strategies like feature combination from multiple DCNNs [21] and supervised embedding methods [22] improving classification and metric learning. The integration of these techniques into tracking systems [23] has enhanced MOT performance through short tracklets and tracklet-plane matching. Furthermore, structured prediction optimizes feature embedding [24]. Although models like ReDeformTR [25] effectively track animals across cameras, they lack the versatility of ReTrackVLM, which tracks various objects without complex feature fusion. Additionally, YOLOv5+DeepSORT [26] enhances underwater tracking accuracy for marine creatures, while [27] employs YOLOv5 with recurrent networks for real-time object tracking in challenging conditions. Furthermore, a triplet-based MOT method exploiting an attention-based ReID module is presented in [28] to enhance object association, particularly in challenging occlusion scenarios. A method proposed in [29], grayscale spatial-temporal features, aims to improve tracking speed and efficiency, especially for devices with limited computing power, and uses a grayscale mapping technique to acquire spatial-temporal features, allowing for direct target localization in previous frames and reducing the computational burden associated with ID matching. In multi-camera tracking, the end-to-end approach in [30] utilizes probabilistic association and detection embeddings to manage scenarios effectively, though it struggles with complex occlusions. The semi-online tracking refinement method in [31] corrects IDSws by monitoring appearance similarity changes over time. Similarly, the multi-class tracking approach in [32] achieves predictable execution times by class-splitting the Hungarian matrix, though this may sacrifice accuracy in dense scenarios. A framework in [33] employs weakly supervised multi-object tracking and segmentation for improved mask consistency, but our method enhances this with a novel embedding structure and track management module.