Propagating prior information with transformer for robust visual object tracking

In recent years, the domain of visual object tracking has witnessed considerable advancements with the advent of deep learning methodologies. Siamese-based trackers have been pivotal, establishing a new architecture with a weight-shared backbone. With the inclusion of the transformer, attention mechanism has been exploited to enhance the feature discriminability across successive frames. However, the limited adaptability of many existing trackers to the different tracking scenarios has led to inaccurate target localization. To effectively solve this issue, in this paper, we have integrated a siamese network with the transformer, where the former utilizes ResNet50 as the backbone network to extract the target features, while the latter consists of an encoder and a decoder, where the encoder can effectively utilize global contextual information to obtain the discriminative features. Simultaneously, we employ the decoder to propagate prior information related to the target, which enables the tracker to successfully locate the target in a variety of environments, enhancing the stability and robustness of the tracker. Extensive experiments on four major public datasets, OTB100, UAV123, GOT10k and LaSOText demonstrate the effectiveness of the proposed method. Its performance surpasses many state-of-the-art trackers. Additionally, the proposed tracker can achieve a tracking speed of 60 fps, meeting the requirements for real-time tracking.