Towards Discriminative Representations with Contrastive Instances for
  Real-Time UAV Tracking
Dan Zeng Mingliang Zou Xucheng Wang Shuiwang Li
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.