Event cameras, or dynamic vision sensors, have recently achieved success from
fundamental vision tasks to high-level vision researches. Due to its ability to
asynchronously capture light intensity changes, event camera has an inherent
advantage to capture moving objects in challenging scenarios including objects
under low light, high dynamic range, or fast moving objects. Thus event camera
are natural for visual object tracking. However, the current event-based
trackers derived from RGB trackers simply modify the input images to event
frames and still follow conventional tracking pipeline that mainly focus on
object texture for target distinction. As a result, the trackers may not be
robust dealing with challenging scenarios such as moving cameras and cluttered
foreground. In this paper, we propose a distractor-aware event-based tracker
that introduces transformer modules into Siamese network architecture (named
DANet). Specifically, our model is mainly composed of a motion-aware network
and a target-aware network, which simultaneously exploits both motion cues and
object contours from event data, so as to discover motion objects and identify
the target object by removing dynamic distractors. Our DANet can be trained in
an end-to-end manner without any post-processing and can run at over 80 FPS on
a single V100. We conduct comprehensive experiments on two large event tracking
datasets to validate the proposed model. We demonstrate that our tracker has
superior performance against the state-of-the-art trackers in terms of both
accuracy and efficiency.

提出了一种引入了变压器模块的具有抗干扰能力的事件驱动跟踪器，在两个大型事件跟踪数据集上验证了其在准确性和效率方面优于现有的最先进跟踪器。

注意分心事件追踪

Distractor-aware Event-based Tracking

Recently, Siamese networks have drawn great attention in visual tracking
community because of their balanced accuracy and speed. However, features used
in most Siamese tracking approaches can only discriminate foreground from the
non-semantic backgrounds. The semantic backgrounds are always considered as
distractors, which hinders the robustness of Siamese trackers. In this paper,
we focus on learning distractor-aware Siamese networks for accurate and
long-term tracking. To this end, features used in traditional Siamese trackers
are analyzed at first. We observe that the imbalanced distribution of training
data makes the learned features less discriminative. During the off-line
training phase, an effective sampling strategy is introduced to control this
distribution and make the model focus on the semantic distractors. During
inference, a novel distractor-aware module is designed to perform incremental
learning, which can effectively transfer the general embedding to the current
video domain. In addition, we extend the proposed approach for long-term
tracking by introducing a simple yet effective local-to-global search region
strategy. Extensive experiments on benchmarks show that our approach
significantly outperforms the state-of-the-arts, yielding 9.6% relative gain in
VOT2016 dataset and 35.9% relative gain in UAV20L dataset. The proposed tracker
can perform at 160 FPS on short-term benchmarks and 110 FPS on long-term
benchmarks.

本文关注于学习一种能够识别语义背景干扰的 Siamese 追踪网络，提出了一种有效的训练策略和增量学习模块，同时引入局部到全局搜索策略，实现了在多个数据集上的显著表现。