Achieving optimal performance with frame-based vision sensors on aerial
platforms poses a significant challenge due to the fundamental tradeoffs
between bandwidth and latency. Event cameras, which draw inspiration from
biological vision systems, present a promising alternative due to their
exceptional temporal resolution, superior dynamic range, and minimal power
requirements. Due to these properties, they are well-suited for processing and
segmenting fast motions that require rapid reactions. However, previous methods
for event-based motion segmentation encountered limitations, such as the need
for per-scene parameter tuning or manual labelling to achieve satisfactory
results. To overcome these issues, our proposed method leverages features from
self-supervised transformers on both event data and optical flow information,
eliminating the need for human annotations and reducing the parameter tuning
problem. In this paper, we use an event camera with HD resolution onboard a
highly dynamic aerial platform in an urban setting. We conduct extensive
evaluations of our framework across multiple datasets, demonstrating
state-of-the-art performance compared to existing works. Our method can
effectively handle various types of motion and an arbitrary number of moving
objects. Code and dataset are available at:
https://samiarja.github.io/evairborne/

通过在事件数据和光流信息上利用自监督变换器，我们提出的方法克服了事件驱动运动分割的限制，消除了人工注释的需求，并减少了参数调整问题，实现了在多个数据集上与现有方法相比的最新性能，适用于高动态航空平台上的高清事件摄像头在城市环境中处理各种类型的运动并处理任意数量的移动对象。