Domain Adaptive Transformer Tracking Under Occlusions
Qianqian Yu, Keqi Fan, Yuhui Zheng
Abstract
Due to their excellent performance on aggregating global features, Transformer structures are being widely employed in deep learning-based visual object tracking algorithms, recently. Nevertheless, existing Transformer-based trackers still fail to handle occlusion problems due to drift in feature distributions. To address this issue, we introduce domain adaptation techniques into a novel object tracking framework, DATransT, including feature extraction, domain adaptive Transformer module and prediction head. The domain adaptive Transformer module consists of three weight-sharing branches with self and cross attention mechanisms: the source, the target and the source-target branches. Specifically, the source-target branch employs cross-attention to effectively align the feature distributions of the source and target branches. Meanwhile, we present a pseudo-labeling strategy to generate high-quality training samples. Extensive experiments show that DATransT obtains promising results on several popular datasets, containing LaSOT, TrackingNet, GOT-10k, NfS, OTB2015 and UAV123. Moreover, our method outperforms existing state-of-the-art trackers under full occlusions and partial occlusions.