OmniGlimpse: Asymmetric Identity Memory for Online Multi-View Tracking
Multi-view tracking extends visual coverage beyond a single camera, providing complementary observations when targets are occluded or leave the view. This raises a question: how can observations from different views be matched to the correct identities without offline scene-specific training or camera calibration? Progress toward this goal remains limited because many offline formulations assume such deployment-specific knowledge, while online adaptation can amplify association errors. We study this problem as online multi-view identity maintenance (OMIM): targets are initialized once per view, with no further identity labels provided after each initialization. To this end, we introduce O...
| Authors | Rangya Zhang, Jiaping Xiao, Yujing Shang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 October 2026 |
| Last updated at source | 27 October 2026 |
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