  Tracking using bio-inspired event cameras has drawn more and more attention
in recent years. Existing works either utilize aligned RGB and event data for
accurate tracking or directly learn an event-based tracker. The first category
needs more cost for inference and the second one may be easily influenced by
noisy events or sparse spatial resolution. In this paper, we propose a novel
hierarchical knowledge distillation framework that can fully utilize
multi-modal / multi-view information during training to facilitate knowledge
transfer, enabling us to achieve high-speed and low-latency visual tracking
during testing by using only event signals. Specifically, a teacher
Transformer-based multi-modal tracking framework is first trained by feeding
the RGB frame and event stream simultaneously. Then, we design a new
hierarchical knowledge distillation strategy which includes pairwise
similarity, feature representation, and response maps-based knowledge
distillation to guide the learning of the student Transformer network.
Moreover, since existing event-based tracking datasets are all low-resolution
($346 \times 260$), we propose the first large-scale high-resolution ($1280
\times 720$) dataset named EventVOT. It contains 1141 videos and covers a wide
range of categories such as pedestrians, vehicles, UAVs, ping pongs, etc.
Extensive experiments on both low-resolution (FE240hz, VisEvent, COESOT), and
our newly proposed high-resolution EventVOT dataset fully validated the
effectiveness of our proposed method. The dataset, evaluation toolkit, and
source code are available on
\url{https://github.com/Event-AHU/EventVOT_Benchmark}
