arXiv Computer Vision
4d ago

ReWorld-Track: A Recursive Event World Model for Language-Guided Multi-Camera Tracking

ReWorld-Track introduces a recursive event world model for language‑guided multi‑camera tracking that explicitly carries association uncertainty into future predictions. By treating candidate matches and waiting as alternative target states, the model updates a persistent recurrent belief that preserves uncertainty across successive observations. This approach improves identity continuity and next‑camera accuracy, achieving HOTA scores of 65.19 on CityFlowV2 and 45.36 on MTMMC, and reducing median arrival‑time error from 0.78 s to 0.71 s.

By Haoyang Wu, Shoudong Han, Chaoyue Li, Sijia Chen, Zhenyang Xie, Wang sihan
arXiv Computer Vision
Sep 3

Learning to Track from Privileged Target Appearances

The paper introduces Privileged Appearance Transfer for Tracking (PATT), a teacher‑student framework that leverages exact target crops from past, current, and future frames during training to improve visual tracking. By weighting the teacher’s guidance with its localization advantage and accuracy, PATT transfers privileged appearance information to a deployable tracker that only uses past‑frame templates at inference. Experiments on seven benchmarks show consistent performance gains across both long‑ and short‑term tracking protocols.

By Xin Chen, Jiao Xu, Dong Wang, Huchuan Lu, Kede Ma