Know Who You Are: Learning Target-Aware Transformer for Object Tracking
Zhuojun Zou, Xuexin Liu, Yuanpei Zhang, Lin Shu, Jie Hao · 2023
Tracking methods for measuring the similarity between the template and search region have achieved great success in recent years. Although many researchers have made efforts to introduce template annotations into network, inductive bias for trackers is unavoidable due to the inherent disadvantage of box representation. In this work, a novel tracking framework is proposed to eliminate the misguidance of biased prior, based on which, a target-aware Transformer tracker is designed. We use the template annotation as a predicted item in supervised learning, train our model to estimate the same target in template and search frame simultaneously, so that the tracker can learn the target-awareness both in the past and present frame. Our method can be assembled on the vast majority of Transformer-based networks. Sufficient experiments on six datasets verify the correctness of the proposed model. Without the bells and whistles, our tracker achieves the state-of-the-art performance on multiple benchmarks.