###### Abstract  
Multi-object tracking (MOT) is an important task in computer vision, particularly in complex, dynamic environments with crowded scenes and frequent occlusions. Traditional tracking methods often suffer from identity switches (IDSws) and fragmented tracks (FMs), which limits their ability to maintain consistent object trajectories. In this paper, we present a novel framework, called ReTrackVLM, that integrates multimodal embedding from a visual language model (VLM) with a zero-shot re-identification (ReID) module to enhance tracking accuracy and robustness. ReTrackVLM leverages the rich semantic information from VLMs to distinguish objects more effectively, even under challenging conditions, while the zero-shot ReID mechanism enables robust identity matching without additional training. The system also includes a motion prediction module, powered by Kalman filtering, to handle object occlusions and abrupt movements. We evaluated ReTrackVLM on several widely used MOT benchmarks, including MOT15, MOT16, MOT17, MOT20, and DanceTrack. Our approach achieves state-of-the-art results, with improvements of 1.5% MOTA and a reduction of 10. 3% in IDSws compared to existing methods. ReTrackVLM also excels in tracking precision, recording a 91.7% precision on MOT17. However, in extremely dense scenes, the framework faces challenges with slight increases in IDSws. Despite the computational overhead of using VLMs, ReTrackVLM demonstrates the ability to track objects effectively in diverse scenarios. m m  
advances and practical deployment in real-world systems. The introduction of large-scale, diverse datasets such as MOT15 [1], MOT16/17 [2], MOT20 [3], DanceTrack [4], and WildTrack [5] has spurred significant progress in this field, enabling the training and evaluation of more sophisticated models. However, despite these advancements, achieving reliable and scalable multi-object tracking remains a formidable challenge, requiring innovative solutions that can adapt to the complexities of real-world scenarios. The choice of datasets is crucial for developing and evaluating MOT algorithms, providing diverse benchmarks that reflect real-world complexities. The MOT Challenge series (MOT15, MOT16, MOT17, MOT20) is central to MOT research, capturing urban scenes with varying pedestrian densities and challenges like occlusions, camera motion, and lighting. While MOT15 laid the foundation, later versions introduced higher-quality annotations and more complex scenarios. DanceTrack tests algorithms on fast, intertwined movements in dance sequences, emphasizing identity maintenance in dynamic situations. WildTrack adds complexity with its multi-camera setup, which is essential for wide-area coverage and testing scalability in applications like sports analytics and surveillance. The diverse and challenging environments captured in the MOT15, MOT16, MOT17, MOT20, DanceTrack, and WildTrack datasets are illustrated in Figure 1, further highlighting the importance of using diverse datasets to evaluate the performance of MOT algorithms since they encompass a broad spectrum of challenges, from standard urban environments to extreme conditions. They serve as invaluable tools for researchers to develop and validate MOT algorithms that are not only effective in controlled settings but are also robust and generalizable to real-world applications. Despite notable advancements in MOT, current methods still face limitations in real-world scenarios. A key issue is IDSws, where trackers fail to maintain object identities, especially in crowded or cluttered settings due to occlusions or objects leaving and re-entering the field of view. Conventional approaches, relying on motion models and appearance-based ReID, struggle with significant appearance changes or unpredictable motions. Many methods also rely on handcrafted features or shallow models that lack robustness and generalization, requiring fine-tuning across datasets.