There is currently strong interest in improving visual object tracking by
augmenting the RGB modality with the output of a visual event camera that is
particularly informative about the scene motion. However, existing approaches
perform event feature extraction for RGB-E tracking using traditional
appearance models, which have been optimised for RGB only tracking, without
adapting it for the intrinsic characteristics of the event data. To address
this problem, we propose an Event backbone (Pooler), designed to obtain a
high-quality feature representation that is cognisant of the innate
characteristics of the event data, namely its sparsity. In particular,
Multi-Scale Pooling is introduced to capture all the motion feature trends
within event data through the utilisation of diverse pooling kernel sizes. The
association between the derived RGB and event representations is established by
an innovative module performing adaptive Mutually Guided Fusion (MGF).
Extensive experimental results show that our method significantly outperforms
state-of-the-art trackers on two widely used RGB-E tracking datasets, including
VisEvent and COESOT, where the precision and success rates on COESOT are
improved by 4.9% and 5.2%, respectively. Our code will be available at
this https URL

通过改进 RGB 模式的视觉对象跟踪，利用视觉事件相机的输出，特别是对场景运动特别有信息量的输出，本文通过引入一个适应事件数据固有特性的事件 backbone (Pooler)，提出了一种高质量的特征表示方法，该方法利用多尺度池化来捕捉事件数据中的所有运动特征趋势，并通过一个创新的模块进行自适应互相指导融合（MGF），在两个广泛使用的 RGB-E 跟踪数据集上，我们的方法在精确度和成功率上分别提高了 4.9% 和 5.2%。