Maintaining high efficiency and high precision are two fundamental challenges in UAV tracking due to the constraints of computing resources, battery capacity, and UAV maximum load. Discriminative correlation filters (DCF)-based trackers can yield high efficiency on a single CPU but with inferior precision. Lightweight Deep learning (DL)-based trackers can achieve a good balance between efficiency and precision but performance gains are limited by the compression rate. High compression rate often leads to poor discriminative representations. To this end, this paper aims to enhance the discriminative power of feature representations from a new feature-learning perspective. Specifically, we attempt to learn more disciminative representations with contrastive instances for UAV tracking in a simple yet effective manner, which not only requires no manual annotations but also allows for developing and deploying a lightweight model. We are the first to explore contrastive learning for UAV tracking. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that the proposed DRCI tracker significantly outperforms state-of-the-art UAV tracking methods.

翻译：在无人机跟踪任务中，由于计算资源、电池容量和无人机最大载荷的限制，保持高效率和高质量是两大基本挑战。基于判别性相关滤波器（DCF）的跟踪器虽能在单一CPU上实现高效率，但精度较低。轻量级深度学习（DL）跟踪器能在效率与精度之间取得良好平衡，但性能提升受限于压缩率。高压缩率往往导致判别性表示能力不足。为此，本文旨在从新的特征学习视角增强特征表示的判别能力。具体而言，我们尝试通过一种简单有效的方法，利用对比实例学习更具判别性的表示用于无人机跟踪，该方法不仅无需人工标注，还能开发并部署轻量级模型。我们是首个探索对比学习在无人机跟踪中应用的研究。在包括UAV123@10fps、DTB70、UAVDT和VisDrone2018在内的四个无人机基准数据集上的大量实验表明，所提出的DRCI跟踪器显著优于最先进的无人机跟踪方法。