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Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline by Wang, Xiao; Wang, Shiao; Tang, Chuanming; Zhu, Lin; Jiang, Bo; Tian, Yonghong; Tang, Jin is a scholarly article available to read on EtoBox.

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

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 ($1280 \times 720$)

Author

Wang, Xiao; Wang, Shiao; Tang, Chuanming; Zhu, Lin; Jiang, Bo; Tian, Yonghong; Tang, Jin

Published

2023

Language

EN