LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking
Qingmao Wei, Bi Zeng, Jianqi Liu, Li He, Guotian Zeng

TL;DR
LiteTrack is a lightweight, transformer-based visual tracker that employs asynchronous feature extraction and encoder layer pruning to achieve high accuracy and real-time performance on edge devices.
Contribution
The paper introduces LiteTrack, a novel tracker that combines asynchronous feature extraction with encoder layer pruning for improved efficiency and accuracy.
Findings
LiteTrack-B4 achieves 65.2% AO on GOT-10k at over 100 fps.
LiteTrack-B9 reaches 72.2% AO on GOT-10k and 82.4% AUC on TrackingNet at 171 fps.
The proposed methods outperform existing lightweight trackers in speed and accuracy.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVisual Attention and Saliency Detection · Advanced Neural Network Applications · Video Surveillance and Tracking Methods
MethodsArtemisinin Optimization based on Malaria Therapy: Algorithm and Applications to Medical Image Segmentation · Pruning
