LiteTrack: Layer Pruning with Asynchronous Feature Extraction for
  Lightweight and Efficient Visual Tracking
Qingmao Wei Bi Zeng Jianqi Liu Li He Guotian Zeng
The recent advancements in transformer-based visual trackers have led to
significant progress, attributed to their strong modeling capabilities.
However, as performance improves, running latency correspondingly increases,
presenting a challenge for real-time robotics applications, especially on edge
devices with computational constraints. In response to this, we introduce
LiteTrack, an efficient transformer-based tracking model optimized for
high-speed operations across various devices. It achieves a more favorable
trade-off between accuracy and efficiency than the other lightweight trackers.
The main innovations of LiteTrack encompass: 1) asynchronous feature extraction
and interaction between the template and search region for better feature
fushion and cutting redundant computation, and 2) pruning encoder layers from a
heavy tracker to refine the balnace between performance and speed. As an
example, our fastest variant, LiteTrack-B4, achieves 65.2% AO on the GOT-10k
benchmark, surpassing all preceding efficient trackers, while running over 100
fps with ONNX on the Jetson Orin NX edge device. Moreover, our LiteTrack-B9
reaches competitive 72.2% AO on GOT-10k and 82.4% AUC on TrackingNet, and
operates at 171 fps on an NVIDIA 2080Ti GPU. The code and demo materials will
be available at https://github.com/TsingWei/LiteTrack.