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arXiv Computer Vision By Zihan Zhou, Sijia Chen, Yanqiu Yu, En Yu, Wenbing Tao

ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking

Read the original on arXiv Computer Vision →

The paper introduces ORMOT, a new task that extends Referring Multi‑Object Tracking to omnidirectional 360° imagery, ensuring full scene context for language‑guided tracking. It presents ORSet, a dataset of 27 omnidirectional scenes with 848 language descriptions and 3,401 annotated objects, and introduces ORTrack, an LVLM‑driven framework that performs zero‑shot detection and robust cross‑frame association. Experiments on ORSet show that ORTrack achieves state‑of‑the‑art performance, establishing a strong baseline for future research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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