Adaptive Continual Learning for Online Visual Object Tracking via Dynamic Grassmannian Appearance Modeling

Online visual object tracking fundamentally constitutes a continual learning challenge, demanding persistent adaptation to target variations within dynamic video streams, while preserving critical features to prevent forgetting. Existing template-based trackers are particularly vulnerable to severe target deformation and drastic background changes in real-world scenarios. Recent explorations enhance adaptability through local update strategies—such as dynamic templates, appearance tokens, and parameter fine-tuning—to address rapid appearance variations. However, these methods inherently propagate target appearance changes without explicit modelling and prioritise short-term adaptation over long-term global representation. Consequently, they fail to balance initial target features with current observations, leading to tracking failure in long-term scenarios. To overcome these limitations, we propose DG-Track, an adaptive continual learning framework leveraging Grassmannian manifold geometry. Specifically, we represent target appearance within a Grassmannian affine subspace and perform continual adaptation via incremental learning. Compared to Euclidean geometry, the Grassmannian manifold captures nonlinear appearance representations, yielding more compact and geometrically consistent temporal dynamics modelling. Furthermore, an adaptive forgetting module dynamically regulates the interplay between the current observed subspace and the initial template, ensuring stable long-term tracking. DG-Track is a plug-and-play solution for online tracking, adding no learnable parameters. Comprehensive experiments with diverse baseline trackers on LaSOT, GOT-10k, TrackingNet, and UAV123 validate the efficacy of our continual learning framework and Grassmannian manifold geometry in enhancing visual object tracking performance. Code is available at https://github.com/xiaoqing0825/DGTrack.