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

Abstract

Tracking with bio-inspired event cameras has garnered increasing interest in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The former incurs higher inference costs while the latter may be susceptible to the impact of noisy events or sparse spatial resolution. In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multimodal / 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 multimodal 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. In particular, since existing event-based tracking datasets are all low-resolution (346 × 260), we propose the first large-scale high-resolution (1280 × 720) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pong, etc. Ex-tensive experiments on both low-resolution (FE240hz, Vi-sEvent, COESOT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method.