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Tracking using bio-inspired event cameras has drawn more and more attentionin recent years. Existing works either utilize aligned RGB and event data foraccurate tracking or directly learn an event-based tracker. The first categoryneeds more cost for inference and the second one may be easily influenced bynoisy events or sparse spatial resolution. In this paper, we propose a novelhierarchical knowledge distillation framework that can fully utilizemulti-modal / multi-view information during training to facilitate knowledgetransfer, enabling us to achieve high-speed and low-latency visual trackingduring testing by using only event signals. Specifically, a teacherTransformer-based multi-modal tracking framework is first trained by feedingthe RGB frame and event stream simultaneously. Then, we design a newhierarchical knowledge distillation strategy which includes pairwisesimilarity, feature representation, and response maps-based knowledgedistillation 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 widerange of categories such as pedestrians, vehicles, UAVs, ping pongs, etc.Extensive experiments on both low-resolution (FE240hz, VisEvent, COESOT), andour newly proposed high-resolution EventVOT dataset fully validated theeffectiveness of our proposed method. The dataset, evaluation toolkit, andsource code are available on\url{https://github.com/Event-AHU/EventVOT_Benchmark}

Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline