Target appearance and motion variations are the primary challenges in visual tracking. To tackle these challenges, top-performing trackers commonly rely on constructing complex appearance or motion models. However, the efficacy of these models in enhancing track performance can be limited by the lack of effective and seamless integration. The utilization of simplistic handcrafted fusion methods may even exacerbate the issue, resulting in a decline in tracking performance. To address this issue, we propose an end-to-end coarse-to-fine verifying approach in our motion-driven tracker. At the coarse level, we developed a motion prediction module (MPM) that efficiently extracts and utilizes motion information by leveraging the differences between adjacent frames. The MPM constructs not only a position prior for the decoder but also hybrid features that combine both motion and appearance. At the fine level, we employ a deformable transformer-based appearance model to accurately verify a local region centered on the predicted locations from the MPM. To further enhance the generalization capability of our tracker, we propose the use of an instance domain discriminator (IDD) during the training phase. This discriminator is based on domain adaptation theory and aims to sharpen the distinction between the target and other instances, thereby improving the robustness of tracking. Experimental results on five popular benchmarks, including GOT10k, LaSOT, TrackingNet, OTB, and VOT, validate the effectiveness of our proposed tracker.

Siamese tracking exploits the template and the search region features to adaptively locate arbitrary objects in the tracking. A noteworthy issue is that both foreground and background mix in the template, and thus a tracker needs to learn what the target is and which pixels belong to it. However, existing trackers cannot effectively exploit the template information, resulting in a deficiency of target information and causing confusion for the tracker regarding which pixels belong to the target. To alleviate this issue, we propose UTrack, a simple and effective algorithm for unambiguous object tracking. UTrack utilizes long-term contextual information to propagate the appearance state of the target so as to explicitly model the apparent information of the target. Additionally, UTrack can resist the appearance change of the target by leveraging the target cues. Moreover, the proposed method uses the refined template to obtain more detailed information about the target and better understand which pixels belong to the target. Extensive experiments and comparisons with competitive trackers on challenging large-scale benchmarks show that our tracker can achieve state-of-the-art performances with real-time running. In particular, UTrack achieves 77.7% AO on GOT-10k.

Template tracking is a typical paradigm to adaptively locate arbitrary objects in the tracking literature. Although existing works present diverse template updating approaches, one of the essential problems of template updating has not been solved effectively, i.e., when and how to update a template. In this work, we treat the updating time as an abnormal moment that indicates the previous template cannot depict the target accurately any more. Thus, we introduce an effective State-Edge Awareness (SEA) module that detect such abnormal moments via unsupervised anomaly detection. To be specific, by retaining multi search frames of a video, SEA firstly analysis the correlation features that generated by the template and search images. Then, it estimates the measurement for abnormal degree that is regarded as the sign for template updating. As a result, our method can not only capture the updating time automatically, but also update the templates effectively. Furthermore, the effectiveness of the proposed method has been verified on a representative CNN-based and Transformer-based tracker, respectively. The experimental results on five popular benchmarks show that our tracker can achieve the state-of-the-art performance.