## 5 Discussion and Conclusions  
MOT in complex environments with dense crowds and unpredictable motion presents significant challenges, including maintaining consistent identities across frames amidst occlusions and abrupt movements. Traditional methods often struggle with IDSws and association failures due to inadequate modeling of appearance and motion. To address these issues, we propose ReTrackVLM, a novel tracking framework that employs cross-modal embeddings and zero-shot ReID to enhance performance. ReTrackVLM combines a VLM with an appearance-based ReID system, effectively modeling both visual and semantic information to distinguish objects during ambiguous detections and occlusions. The zero-shot ReID capability enables accurate object matching without additional fine-tuning, relying on previously stored track data. Additionally, a Kalman filter-based motion prediction module estimates object positions between frames, reducing track fragmentation. Extensive evaluations on benchmark datasets--MOT15, MOT16/17, MOT20, and DanceTrack--demonstrate that ReTrackVLM achieves competitive performance, notably excelling in MOTA with 79.7% on MOT16/17. It shows strong results in minimizing IDSws on DanceTrack (684) and WildTrack (761) and in minimizing PT on MOT15 (286) and MOT16/17 (428). However, it has limitations, such as more IDSws on dense and occluded datasets like MOT20, indicating that the zero-shot ReID struggles in such scenarios. Additionally, the motion prediction model could be improved for complex motion patterns, and \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset/VLM** & **MOTA \(\uparrow\)** & **MOT \(\uparrow\)** & **IDSw \(\downarrow\)** & **FM \(\downarrow\)** & **MT \(\uparrow\)** & **PT \(\uparrow\)** & **ML \(\downarrow\)** \\ \hline \multicolumn{8}{c}{**DanceTrack**} \\ \hline DeepSort [60] & 82.2 & 84.5 & 1288 & 903 & 355 & 40 & 257 \\ ByteTrack [61] & 88.7 & 84.8 & 911 & 878 & 389 & 47 & 288 \\ Deep-OC-SORT [62] & 90.3 & 95.4 & 776 & 803 & 393 & 44 & 264 \\ StrongSORT [63] & 88.6 & 83.1 & 809 & 867 & 346 & 52 & 227 \\ BoostTrack [46] & 89.5 & 94.7 & 852 & 905 & 367 & 54 & 249 \\ SFSORT [64] & 89.1 & 94.9 & 964 & 824 & 404 & 42 & 234 \\ Hybrid-SORT [36] & 90.8 & **96.0** & 726 & 812 & 411 & 50 & 253 \\ UCMCTrack [65] & 87.9 & 94.2 & 823 & **699** & **473** & 36 & 201 \\ TrackFormer [66] & 78.4 & 81.6 & 1137 & 984 & 428 & 32 & 302 \\ TransTrack [67] & 87.1 & 96.7 & 1522 & 963 & 509 & **61** & 214 \\ MOTR [68] & 81.2 & 85.3 & 1439 & 1086 & 374 & 46 & 276 \\ LTrack [69] & 84.9 & 83.4 & 1320 & 693 & 344 & 58 & 199 \\
**ReTrackVLM (Ours)** & **91.2** & 91.6 & **684** & **277** & 257 & 60 & **111** \\ \hline \multicolumn{8}{c}{**WildTrack**} \\ \hline DeepSort [60] & 59.2 & 52.5 & 1422 & 1006 & 259 & 180 & 425 \\ ByteTrack [61] & 68.7 & **75.3** & 1037 & 924 & 389 & 187 & **338** \\ Deep-OC-SORT [62] & 65.3 & 59.8 & 1615 & 1064 & 307 & 196 & 438 \\ StrongSORT [63] & 66.9 & 68.9 & 1240 & 1124 & 365 & 185 & 427 \\ BoostTrack [46] & 64.4 & 69.1 & 962 & 1082 & 287 & 222 & 495 \\ SFSORT [64] & 67.8 & 72.2 & 807 & 978 & 295 & 237 & 504 \\ Hybrid-SORT [36] & 65.8 & 70.6 & 789 & **861** & 436 & 239 & 539 \\ UCMCTrack [65] & 61.2 & 61.8 & 973 & 1246 & 488 & **325** & 441 \\ TrackFormer [66] & 60.3 & 63.3 & 1379 & 1177 & 379 & 164 & 386 \\ TransTrack [67] & 67.9 & 73.4 & 1023 & 936 & 502 & 192 & 363 \\ MOTR [68] & 62.4 & 66.7 & 1154 & 1103 & 474 & 178 & 352 \\ LTrack [69] & 63.3 & 65.8 & 939 & 1048 & 421 & 284 & 461 \\
**ReTrackVLM (Ours)** & **69.1** & 71.0 & **761** & 946 & **561** & 204 & 509 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._the reliance on VLMs and cross-modal embeddings introduces computational overhead that may impact real-time tracking applications. While the current study demonstrates the efficacy of the proposed framework on MOT benchmarks and controlled environments, its applicability to unstructured scenes and diverse object types remains unexplored.