ODONet: Online Dynamic Offset Network for Visual Object Tracking

Offset learning has recently demonstrated remarkable prowessin capturing the structural diversity of objects. Consequently, it ap-pears to be a natural fit for modeling the discriminative features ofvisual targets in tracking. Yet existing leading offline offset-based meth-ods struggle to cope with the ever-changing appearance and motionof the target during tracking. To address this limitation, we presentODONet, a Transformer-based tracker that performs online dynamicoffset network. Its Dynamic Visual–Motion Feature Interaction (DV-MFI) method fuses target appearance with historical box information,yielding appearance features and motion-aware embeddings. These arethen fed to an Online Dynamic Offset Propagation (ODOP) methodthat explicitly captures inter-frame motion trends, augments them withoffline offsets, and steers deformable attention toward an adaptive re-ceptive field. To our knowledge, ODONet is the first tracker to prop-agate dynamic offsets online, with excellent performance on publiclyavailable datasets, demonstrating its robustness to appearance and mo-tion drift. Code, models, and additional information are available athttps://github.com/WhiteButterflies/ODONet.