AI RESEARCH
Seg2Track++: Probabilistic Track Validation and Data Association for Multi-Object Tracking and Segmentation
arXiv CS.CV
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ArXi:2606.03875v1 Announce Type: new Autonomous systems require robust Multi-Object Tracking and Segmentation (MOTS) to operate reliably in dynamic environments, ensuring consistent object identities and precise mask-level delineation. Foundation models such as SAM2 have shown strong zero-shot generalization for segmentation, but their direct application to MOTS is limited by unreliable track association and false-positive propagation. This work