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The recent advancements in transformer-based visual trackers have led tosignificant 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 edgedevices with computational constraints. In response to this, we introduceLiteTrack, an efficient transformer-based tracking model optimized forhigh-speed operations across various devices. It achieves a more favorabletrade-off between accuracy and efficiency than the other lightweight trackers.The main innovations of LiteTrack encompass: 1) asynchronous feature extractionand interaction between the template and search region for better featurefushion and cutting redundant computation, and 2) pruning encoder layers from aheavy tracker to refine the balnace between performance and speed. As anexample, our fastest variant, LiteTrack-B4, achieves 65.2% AO on the GOT-10kbenchmark, surpassing all preceding efficient trackers, while running over 100fps with ONNX on the Jetson Orin NX edge device. Moreover, our LiteTrack-B9reaches competitive 72.2% AO on GOT-10k and 82.4% AUC on TrackingNet, andoperates at 171 fps on an NVIDIA 2080Ti GPU. The code and demo materials willbe available at https://github.com/TsingWei/LiteTrack.

LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking