基于SAM2的多目标跟踪分割框架，提升身份一致性与内存效率。

Autonomous-driving perception systems require robust Multi-Object Tracking (MOT) to operate reliably in dynamic environments. MOT maintains consistent object identities across frames while preserving spatial accuracy. Recent foundation models, such as SAM2, provide promptable video segmentation without task-specific fine-tuning. However, their direct application to Multi-Object Tracking and Segmentation (MOTS) remains limited by the absence of explicit identity management mechanisms and by growing memory requirements during tracking. This work introduces Seg2Track-SAM2, a framework that integrates pretrained object detectors with SAM2 and a dedicated Seg2Track module to support track initialization, data association, and track refinement. The method operates without dataset-specific fine-tuning and remains detector-agnostic. Experimental evaluation on the KITTI MOTS and MOTS Challenge benchmarks shows that Seg2Track-SAM2 ranks fourth overall in both datasets while achieving the highest association accuracy (AssA) among compared methods. In addition, a sliding-window memory strategy reduces memory usage by up to 75% with minimal impact on tracking performance, enabling deployment under resource constraints. Together, these results indicate that Seg2Track-SAM2 improves identity consistency and memory efficiency in MOTS without requiring dataset-specific training. The code is available at https://github.com/hcmr-lab/Seg2Track-SAM2.