## 4 Experiments and Results
### Ablation Study
CLIP consistently outperforms other VLMs, achieving the highest MOTA and MOTP, especially on DanceTrack (61.8%) and WildTrack (69.9%), along with the lowest IDSw and FM, demonstrating superior track consistency. DETR2 closely follows, particularly on MOT16/17 and MOT20, balancing higher MT values with lower ML rates. OpenCLIP and Florence-2 generally lag behind, with higher IDSw and FM; however, Florence-2 performs reasonably on MOT16/17 and WildTrack. These findings highlight CLIP's robustness in maintaining identity consistency in zero-shot ReID scenarios. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset/VLM** & **MOTA \(\uparrow\)** & **MOTP \(\uparrow\)** & **IDSw \(\downarrow\)** & **FM \(\downarrow\)** & **MT \(\uparrow\)** & **PT \(\uparrow\)** & **ML \(\downarrow\)** \\ \hline \multicolumn{8}{c}{**MOT15**} \\ \hline OpenCLIP & 56.4 & 58.7 & 1328 & 1420 & 169 & 65 & 296 \\ Florence-2 & 58.9 & 60.2 & 1188 & 1241 & 175 & 70 & 283 \\ DETR2 & 60.5 & 63.3 & 1173 & 1201 & 178 & 54 & 241 \\ CLIP & 60.3 & 64.0 & 994 & 1103 & 201 & 61 & 208 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Zero-shot ReID performance metrics with no fine-tuned YOLOv8 and confident tracks (no fine-tuned VLMs). The track management was handled via DeepSort. Arrows indicate whether higher or lower values are better for each metric. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset/VLM** & **MOTA \(\uparrow\)** & **MOTP \(\uparrow\)** & **IDSw \(\downarrow\)** & **FM \(\downarrow\)** & **MT \(\uparrow\)** & **PT \(\uparrow\)** & **ML \(\downarrow\)** \\ \hline \multicolumn{8}{c}{**MOT15**} \\ \hline OpenCLIP & 53.7 & 56.3 & 2654 & 2418 & 144 & 63 & 251 \\ Florence-2 & 57.2 & 59.6 & 1461 & 2365 & 152 & 64 & 248 \\ DETR2 & 58.2 & 60.9 & 1520 & 2388 & 150 & 60 & 240 \\ CLIP & 56.3 & 59.2 & 1211 & 2303 & 181 & 54 & 233 \\ \hline \multicolumn{8}{c}{**MOT16/17**} \\ \hline OpenCLIP & 49.6 & 50.4 & 2088 & 2714 & 707 & 172 & 693 \\ Florence-2 & 52.2 & 56.8 & 1954 & 2839 & 725 & 170 & 609 \\ DETR2 & 54.5 & 57.9 & 1876 & 2792 & 816 & 163 & 555 \\ CLIP & 58.3 & 59.2 & 1934 & 2817 & 804 & 168 & 542 \\ \hline \multicolumn{8}{c}{**MOT20**} \\ \hline OpenCLIP & 45.7 & 47.1 & 2526 & 2816 & 230 & 188 & 307 \\ Florence-2 & 48.2 & 47.6 & 2699 & 2054 & 266 & 129 & 295 \\ DETR2 & 50.1 & 53.3 & 1561 & 2234 & 312 & 120 & 324 \\ CLIP & 48.5 & 50.5 & 1453 & 1961 & 334 & 132 & 273 \\ \hline \multicolumn{8}{c}{**DanceTrack**} \\ \hline OpenCLIP & 51.6 & 52.3 & 1725 & 1511 & 304 & 24 & 5 \\ Florence-2 & 50.2 & 54.7 & 1881 & 1532 & 327 & 27 & 5 \\ DETR2 & 50.4 & 53.1 & 1486 & 1567 & 336 & 35 & 4 \\ CLIP & 54.0 & 55.5 & 1261 & 1389 & 349 & 36 & 3 \\ \hline \multicolumn{8}{c}{**WildTrack**} \\ \hline OpenCLIP & 57.3 & 59.1 & 1322 & 925 & 342 & 34 & 269 \\ Florence-2 & 60.8 & 63.6 & 1294 & 889 & 308 & 47 & 278 \\ DETR2 & 64.4 & 67.2 & 1236 & 1007 & 343 & 38 & 253 \\ CLIP & 67.9 & 70.8 & 1285 & 902 & 306 & 41 & 284 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Baseline performance metrics for different VLMs in ReTrackVLM. The detector is YOLOv8 for all configurations, no ReID is applied, and track management is handled by a standard DeepSort configuration. Arrows indicate whether a higher or lower value is better for each metric. In Figure 7, we provide a detailed visualization of the tracking performance of RecTrackVLM across five benchmark datasets: DanceTrack, MOT15, MOT16, MOT17, and MOT20. These datasets represent a variety of challenging scenarios, including dynamic and static pedestrians, different camera movements, and varying levels of crowd density. The tracking results for each dataset are shown for specific frames: the first frame (t = 1), the 100 th frame (t = 100), the 200 th frame (t = 200), and the final frame (t = N). However, for MOT20, the last frame is excluded due to extreme occlusion and track overlap. Beneath the visualizations, tracking metric graphs illustrate the number of tracks per frame, offering a temporal analysis of trajectory continuity and dataset complexity.