Asynchronous Corner Detection and Tracking for Event Cameras in Real Time
Asynchronous Corner Detection and Tracking for Event Cameras in Real Time
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DOI：
10.1109/lra.2018.2849882
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发表时间：
2018-10-01
影响因子：
5.2
通讯作者：
Chli, Margarita
中科院分区：
文献类型：
--
作者：
Alzugaray, Ignacio;Chli, Margarita
最近出现的仿生事件相机为高频跟踪开辟了令人兴奋的新可能性，为传统视觉中的常见问题带来了鲁棒性，例如照明变化和运动模糊。为了利用事件摄像机的这些有吸引力的属性，研究一直专注于了解如何处理它们不寻常的输出：异步事件流。大多数现有技术离散化事件流，基本上形成根据其时间戳分组的事件帧，我们仍然要利用这些相机的力量。本着这种精神，这封信提出了一个新的，纯粹基于事件的角点检测器，和一个新颖的角点跟踪器，证明它是可能的检测角点，并直接跟踪他们的事件流真实的时间。对基准数据集的评估显示，即使在具有挑战性的场景中，使用所提出的方法，检测到的拐角数量和此类检测的可重复性也比现有技术有显着提高，同时与最有效的算法相比，速度可提高4倍以上。文献中。拟议的管道以每秒超过750万个事件的速度检测和跟踪拐角，有望在高速应用中产生巨大影响。
The recent emergence of bioinspired event cameras has opened up exciting new possibilities in high-frequency tracking, bringing robustness to common problems in traditional vision, such as lighting changes and motion blur. In order to leverage these attractive attributes of the event cameras, research has been focusing on understanding how to process their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream essentially forming frames of events grouped according to their timestamp, we are still to exploit the power of these cameras. In this spirit, this letter proposes a new, purely event-based corner detector, and a novel corner tracker, demonstrating that it is possible to detect corners and track them directly on the event streamin real time. Evaluation on benchmarking datasets reveals a significant boost in the number of detected corners and the repeatability of such detections over the state of the art even in challenging scenarios with the proposed approach while enabling more than a 4x speed-up when compared to the most efficient algorithm in the literature. The proposed pipeline detects and tracks corners at a rate of more than 7.5 million events per second, promising great impact in high-speed applications.