Exploring Lightweight Hierarchical Vision Transformers for Efficient
  Visual Tracking
Ben Kang Xin Chen Dong Wang Houwen Peng Huchuan Lu
Transformer-based visual trackers have demonstrated significant progress
owing to their superior modeling capabilities. However, existing trackers are
hampered by low speed, limiting their applicability on devices with limited
computational power. To alleviate this problem, we propose HiT, a new family of
efficient tracking models that can run at high speed on different devices while
retaining high performance. The central idea of HiT is the Bridge Module, which
bridges the gap between modern lightweight transformers and the tracking
framework. The Bridge Module incorporates the high-level information of deep
features into the shallow large-resolution features. In this way, it produces
better features for the tracking head. We also propose a novel dual-image
position encoding technique that simultaneously encodes the position
information of both the search region and template images. The HiT model
achieves promising speed with competitive performance. For instance, it runs at
61 frames per second (fps) on the Nvidia Jetson AGX edge device. Furthermore,
HiT attains 64.6% AUC on the LaSOT benchmark, surpassing all previous efficient
trackers.