Visual Multi-Object Tracking (MOT) is a crucial component of robotic perception, yet existing Tracking-By-Detection (TBD) methods often rely on 2D cues, such as bounding boxes and motion modeling, which struggle under occlusions and close-proximity interactions. Trackers relying on these 2D cues are particularly unreliable in robotic environments, where dense targets and frequent occlusions are common. While depth information has the potential to alleviate these issues, most existing MOT datasets lack depth annotations, leading to its underexploited role in the domain. To unveil the potential of depth-informed trajectory refinement, we introduce DepTR-MOT, a category-agnostic DETR-based detector enhanced with instance-level depth information. Specifically, we propose two key innovations: (i) foundation model-based instance-level soft depth label supervision, which refines depth prediction, and (ii) the distillation of dense depth maps to maintain global depth consistency. These strategies enable DepTR-MOT to embed instance-level depth awareness into detector and to output instance-level depth during inference. All designs strictly follow the prevailing Tracking-By-Detection (TBD) paradigm, requiring no additional training or inference stages. By incorporating depth cues, our method enhances the robustness of the TBD paradigm and effectively resolves occlusion and close-proximity challenges. Experiments on the QuadTrack, DanceTrack, SportsMOT, and VisDrone19 datasets demonstrate the effectiveness of our approach, achieving HOTA scores of 27.59, 44.939, 61.2, and 42.5, respectively. These consistent gains across aerial, sports, and surveillance platforms highlight the advantages of our method in handling occlusion and close-proximity challenges in robotic tracking. The source code will be made publicly available at https://github.com/warriordby/DepTR-MOT.

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