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}

翻译：利用仿生事件相机进行目标追踪近年来引起了越来越多的关注。现有工作或利用对齐的RGB与事件数据实现精准跟踪，或直接学习基于事件的跟踪器。第一类方法推理成本较高，第二类方法则易受噪声事件或稀疏空间分辨率影响。本文提出一种新颖的层次化知识蒸馏框架，能够在训练时充分利用多模态/多视角信息促进知识迁移，从而在测试阶段仅使用事件信号即可实现高速、低延迟的视觉跟踪。具体而言，首先通过同时输入RGB帧与事件流训练一个基于Transformer的多模态教师跟踪框架。随后，我们设计了一种包含成对相似性、特征表示及响应图的层次化知识蒸馏策略，用于指导学生Transformer网络的学习。此外，鉴于现有基于事件的跟踪数据集均为低分辨率 ($346 \times 260$)，我们首次提出大规模高分辨率 ($1280 \times 720$) 数据集EventVOT，包含1141个视频，覆盖行人、车辆、无人机、乒乓球等广泛类别。在低分辨率数据集（FE240hz、VisEvent、COESOT）以及我们新提出的高分辨率EventVOT数据集上的大量实验充分验证了所提方法的有效性。数据集、评估工具包及源代码均发布于 \url{https://github.com/Event-AHU/EventVOT_Benchmark}。