Event-based object detection has recently garnered attention in the computer
vision community due to the exceptional properties of event cameras, such as
high dynamic range and no motion blur. However, feature asynchronism and
sparsity cause invisible objects due to no relative motion to the camera,
posing a significant challenge in the task. Prior works have studied various
memory mechanisms to preserve as many features as possible at the current time,
guided by temporal clues. While these implicit-learned memories retain some
short-term information, they still struggle to preserve long-term features
effectively. In this paper, we consider those invisible objects as
pseudo-occluded objects and aim to reveal their features. Firstly, we introduce
visibility attribute of objects and contribute an auto-labeling algorithm to
append additional visibility labels on an existing event camera dataset.
Secondly, we exploit tracking strategies for pseudo-occluded objects to
maintain their permanence and retain their bounding boxes, even when features
have not been available for a very long time. These strategies can be treated
as an explicit-learned memory guided by the tracking objective to record the
displacements of objects across frames. Lastly, we propose a spatio-temporal
feature aggregation module to enrich the latent features and a consistency loss
to increase the robustness of the overall pipeline. We conduct comprehensive
experiments to verify our method's effectiveness where still objects are
retained but real occluded objects are discarded. The results demonstrate that
(1) the additional visibility labels can assist in supervised training, and (2)
our method outperforms state-of-the-art approaches with a significant
improvement of 7.9% absolute mAP.

在这篇论文中，我们考虑将不可见的物体视为伪遮挡物体，并旨在揭示它们的特征。我们提出了可视属性和自动标注算法来处理现有事件相机数据集，并利用跟踪策略来保持伪遮挡物体的永久性和边界框，同时引入了时空特征聚合模块和一致性损失以提高整体流程的稳健性。实验证明，额外的可见性标签可以辅助有监督训练，并且我们的方法在绝对 mAP 上比最先进的方法表现出显著的改进，提高了 7.9%。

基于轨迹辅助的事件摄像头物体检测

Tracking-Assisted Object Detection with Event Cameras

Tracking by detection, the dominant approach for online multi-object
tracking, alternates between localization and association steps. As a result,
it strongly depends on the quality of instantaneous observations, often failing
when objects are not fully visible. In contrast, tracking in humans is
underlined by the notion of object permanence: once an object is recognized, we
are aware of its physical existence and can approximately localize it even
under full occlusions. In this work, we introduce an end-to-end trainable
approach for joint object detection and tracking that is capable of such
reasoning. We build on top of the recent CenterTrack architecture, which takes
pairs of frames as input, and extend it to videos of arbitrary length. To this
end, we augment the model with a spatio-temporal, recurrent memory module,
allowing it to reason about object locations and identities in the current
frame using all the previous history. It is, however, not obvious how to train
such an approach. We study this question on a new, large-scale, synthetic
dataset for multi-object tracking, which provides ground truth annotations for
invisible objects, and propose several approaches for supervising tracking
behind occlusions. Our model, trained jointly on synthetic and real data,
outperforms the state of the art on KITTI and MOT17 datasets thanks to its
robustness to occlusions.

该论文提出了一种端到端可训练的方法，结合物体检测和跟踪，并引入了序列记忆模块，使其更好地处理多物体跟踪，尤其是遮挡的情况。经过大规模的合成数据和实际测试，在 KITTI 和 MOT17 数据集上取得了最先进的性能表现。