Event cameras are a novel type of biologically inspired vision sensor known
for their high temporal resolution, high dynamic range, and low power
consumption. Because of these properties, they are well-suited for processing
fast motions that require rapid reactions. Although event cameras have recently
shown competitive performance in unsupervised optical flow estimation,
performance in detecting independently moving objects (IMOs) is lacking behind,
although event-based methods would be suited for this task based on their low
latency and HDR properties. Previous approaches to event-based IMO segmentation
have been heavily dependent on labeled data. However, biological vision systems
have developed the ability to avoid moving objects through daily tasks without
being given explicit labels. In this work, we propose the first event framework
that generates IMO pseudo-labels using geometric constraints. Due to its
unsupervised nature, our method can handle an arbitrary number of not
predetermined objects and is easily scalable to datasets where expensive IMO
labels are not readily available. We evaluate our approach on the EVIMO dataset
and show that it performs competitively with supervised methods, both
quantitatively and qualitatively.

通过几何约束，我们提出了一种基于事件感知的独立运动物体伪标签生成的无监督方法，可以处理任意数量的无先验对象，并能够在缺乏昂贵的物体运动标签的数据集上进行可扩展的评估。我们在 EVIMO 数据集上进行了验证，并从定量和定性上表明其与有监督方法具有竞争性。

无监督事件驱动独立运动分割

Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation

Identifying independently moving objects is an essential task for dynamic
scene understanding. However, traditional cameras used in dynamic scenes may
suffer from motion blur or exposure artifacts due to their sampling principle.
By contrast, event-based cameras are novel bio-inspired sensors that offer
advantages to overcome such limitations. They report pixelwise intensity
changes asynchronously, which enables them to acquire visual information at
exactly the same rate as the scene dynamics. We develop a method to identify
independently moving objects acquired with an event-based camera, i.e., to
solve the event-based motion segmentation problem. We cast the problem as an
energy minimization one involving the fitting of multiple motion models. We
jointly solve two subproblems, namely event cluster assignment (labeling) and
motion model fitting, in an iterative manner by exploiting the structure of the
input event data in the form of a spatio-temporal graph. Experiments on
available datasets demonstrate the versatility of the method in scenes with
different motion patterns and number of moving objects. The evaluation shows
state-of-the-art results without having to predetermine the number of expected
moving objects. We release the software and dataset under an open source
licence to foster research in the emerging topic of event-based motion
segmentation.

本研究开发了一种基于事件的相机的方法，将问题转化为涉及多个运动模型的能量最小化问题，并利用时空图的形式下的输入事件数据的结构迭代地解决事件聚类分配和运动模型拟合两个子问题。实验结果表明，该方法具有很高的灵活性，在不同的场景中能够获取不同运动模式和移动物体的数量。