The unique complementarity of frame-based and event cameras for high frame
rate object tracking has recently inspired some research attempts to develop
multi-modal fusion approaches. However, these methods directly fuse both
modalities and thus ignore the environmental attributes, e.g., motion blur,
illumination variance, occlusion, scale variation, etc. Meanwhile, no
interaction between search and template features makes distinguishing target
objects and backgrounds difficult. As a result, performance degradation is
induced especially in challenging conditions. This paper proposes a novel and
effective Transformer-based event-guided tracking framework, called
eMoE-Tracker, which achieves new SOTA performance under various conditions. Our
key idea is to disentangle the environment into several learnable attributes to
dynamically learn the attribute-specific features for better interaction and
discriminability between the target information and background. To achieve the
goal, we first propose an environmental Mix-of-Experts (eMoE) module that is
built upon the environmental Attributes Disentanglement to learn
attribute-specific features and environmental Attributes Gating to assemble the
attribute-specific features by the learnable attribute scores dynamically. The
eMoE module is a subtle router that fine-tunes the transformer backbone more
efficiently. We then introduce a contrastive relation modeling (CRM) module to
improve interaction and discriminability between the target information and
background. Extensive experiments on diverse event-based benchmark datasets
showcase the superior performance of our eMoE-Tracker compared to the prior
arts.

提出了一种基于变换器的事件引导跟踪框架 (eMoE-Tracker)，通过学习环境的可学习属性来动态学习属性特定的特征，从而改善目标信息和背景之间的交互和可辨识度，使得在各种条件下都能取得新的 SOTA 性能。