TRTrack: Boosting UAV Object Tracking with Voxel-based Trajectory-aware Reconstruction Training

November 15, 2022 · View on GitHub

Abstract

Siamese network-based object tracking has remarkably promoted the automatic capability for highlymaneuvered unmanned aerial vehicles (UAVs). However, the leading-edge tracking framework often depends on template matching, making it trapped when facing multiple views of object in consecutive frames. Moreover, the general imagelevel pretrained backbone can overfit to holistic representations, causing the misalignment to learn object-level properties in UAV tracking. To tackle these issues, this work presents TRTrack, a comprehensive framework to fully exploit the stereoscopic representation for UAV tracking. Specifically, a novel pretraining paradigm method is proposed. Through trajectoryaware reconstruction training, the capability of the backbone to extract stereoscopic structure feature is strengthened without any parameter increment. Accordingly, an innovative hierarchical self-attention Transformer is proposed to capture the local detail information and global structure knowledge. For optimizing the correlation map, we proposed a novel spatial correlation refinement (SCR) module, which promotes the capability of modeling the long-range spatial dependencies. Comprehensive experiments on three UAV challenging benchmarks demonstrate that the proposed TRTrack achieves superior UAV tracking performance in both precision and efficiency. Quantitative tests in real-world settings fully prove the effectiveness of our work.. The demo videos are available at: https://www.bilibili.com/video/BV1PR4y1f74w/?vd_source=100fb1435454490a564488d3d32f4e88.