基于连续性感知的潜在帧间信息挖掘的可靠UAV跟踪
Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking
摘要：
无人机跟踪是无人机自主导航的关键，在机器人自动化领域有着广泛的应用。然而，可靠的无人机跟踪仍然是一个具有挑战性的任务，由于各种困难，如频繁的遮挡和纵横比的变化。此外，大多数现有的工作主要集中在显式信息，以提高跟踪性能，忽略了潜在的帧间连接。为了解决上述问题，这项工作提出了一种新的框架与连续意识的潜在帧间信息挖掘可靠的无人机跟踪，即，ClimRT。提出了一种新的基于连续性的隐帧间信息挖掘网络ClimNet，用于无人机跟踪，该网络能够在相邻帧间生成高效的隐帧。此外，一种新的位置连续性Transformer（LCT）的设计，充分利用连续性感知的时空信息，从而显着提高无人机跟踪。在三个权威的航空基准上进行的大量定性和定量实验有力地验证了ClimRT在无人机跟踪性能方面的鲁棒性和可靠性。最后，通过高空作业平台的实际测试，验证了该方法的实用性和有效性。代码和演示材料在https://github.com/vision4robotics/ClimRT上发布。
英文摘要：
Unmanned aerial vehicle (UAV) tracking is crucial for autonomous navigation and has broad applications in robotic automation fields. However, reliable UAV tracking remains a challenging task due to various difficulties like frequent occlusion and aspect ratio change. Additionally, most of the existing work mainly focuses on explicit information to improve tracking performance, ignoring potential interframe connections. To address the above issues, this work proposes a novel framework with continuity-aware latent interframe information mining for reliable UAV tracking, i.e., ClimRT. Specifically, a new efficient continuity-aware latent interframe information mining network (ClimNet) is proposed for UAV tracking, which can generate highly-effective latent frame between two adjacent frames. Besides, a novel location-continuity Transformer (LCT) is designed to fully explore continuity-aware spatial-temporal information, thereby markedly enhancing UAV tracking. Extensive qualitative and quantitative experiments on three authoritative aerial benchmarks strongly validate the robustness and reliability of ClimRT in UAV tracking performance. Furthermore, real-world tests on the aerial platform validate its practicability and effectiveness. The code and demo materials are released at https://github.com/vision4robotics/ClimRT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
