Existing datasets for RGB-DVS tracking are collected with DVS346 camera and
their resolution ($346 \times 260$) is low for practical applications.
Actually, only visible cameras are deployed in many practical systems, and the
newly designed neuromorphic cameras may have different resolutions. The latest
neuromorphic sensors can output high-definition event streams, but it is very
difficult to achieve strict alignment between events and frames on both spatial
and temporal views. Therefore, how to achieve accurate tracking with unaligned
neuromorphic and visible sensors is a valuable but unresearched problem. In
this work, we formally propose the task of object tracking using unaligned
neuromorphic and visible cameras. We build the first unaligned frame-event
dataset CRSOT collected with a specially built data acquisition system, which
contains 1,030 high-definition RGB-Event video pairs, 304,974 video frames. In
addition, we propose a novel unaligned object tracking framework that can
realize robust tracking even using the loosely aligned RGB-Event data.
Specifically, we extract the template and search regions of RGB and Event data
and feed them into a unified ViT backbone for feature embedding. Then, we
propose uncertainty perception modules to encode the RGB and Event features,
respectively, then, we propose a modality uncertainty fusion module to
aggregate the two modalities. These three branches are jointly optimized in the
training phase. Extensive experiments demonstrate that our tracker can
collaborate the dual modalities for high-performance tracking even without
strictly temporal and spatial alignment. The source code, dataset, and
pre-trained models will be released at
this https URL

使用不对齐的神经形态和可见光相机进行目标跟踪，提出了一种新的跟踪框架，能够通过提取 RGB 和 Event 数据的特征，并利用不确定性感知模块和融合模块实现高性能的跟踪。