Reconstructed and Simulated Dataset for Aerial RGBD Tracking

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across commercial, military, and public safety domains, where target tracking is crucial for imagery content analysis and various applications. This letter explores the challenges in UAV target tracking, particularly addressing deficiencies in RGBD tracking datasets and algorithms for aerial targets. We introduce a new dataset, the Reconstructed and Simulated Aerial RGBD Tracking Dataset (RSTrack), created by reconstructing an existing aerial tracking dataset through depth estimation and enhancing it with simulation environments, containing 209 challenging sequences with over 247 K frames. Experiments conducted on RSTrack demonstrate the effectiveness of depth information in improving aerial target tracking and the benefits of simulation data in enhancing the performance of RGBD trackers in UAV applications. Furthermore, we develop Shallow Feature Fusion-based RGBD Tracking (SFT). Our results indicate that SFT achieves an optimal balance between robustness and speed for UAV applications. These findings are expected to offer a valuable dataset for RGBD tracking and advance methodologies in the field of aerial target tracking. All resources are available at https://github.com/TonikLeung/RSTrack.