Towards Reliable Tracking: An Uncertainty-Aware Siamese Tracker

The recent years have witnessed the great advancement in accuracy made by Siamese trackers, while the performance is still limited by the insufficiency in robustness. Most of existing Siamese trackers regard their model output as deterministic results without explicitly estimating or optimizing the confidence level or reliability. As such, unreliable tracking results may be still utilized in model learning, and thus degrade the tracking model. In this paper, we introduce the notion of reliability into the tracking model, and propose a new tracking framework called Uncertainty-Aware Siamese Network to address this problem. Specifically, within the framework, we introduce the uncertainty awareness into the tracking model by designing a new network structure, which explicitly characterizes the confidence level about the tracker outputs in terms of regression accuracy and classification discriminability. In addition, with the uncertainty estimation module, we propose a new collaborative optimization model which aims to optimize tracking models under the constraint of reliability to make the tracking results as reliable as possible. Furthermore, based on the uncertainty estimation results, an online model updating scheme is developed to ensure both the adaptability and reliability of the model in dealing with appearance variations. Experiments performed on eight standard visual tracking benchmarks, including VOT2018, VOT2019, OTB100, NFS, UAV123, LaSOT, TrackingNet and Got-10 k, show that our uncertainty-aware Siamese tracker achieves state-of-the-art performance.