Aerial Visual Tracking using Multi-level Temporal Contexts Information

Temporal context of video is important for robust visual tracking under complex conditions such as large variations in objects. Existing transformer-based methods focus on updating temporal context information only in search and response map levels that can cause mismatch between search and template. In this paper, we propose an improved visual tracking method applying the template memory update and the multi-aspect loss training method to the existing method. Evaluation on three benchmarks shows that our method outperforms others.