AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision

• URL: http://arxiv.org/abs/2103.11645v1
• Date: Mon, 22 Mar 2021 08:09:03 GMT
• Title: AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision
• Authors: Chang Liu, Xiaojuan Qi, Edmund Lam, Ngai Wong
• Abstract summary: Event data are noisy, sparse, and nonuniform in the spatial-temporal domain with an extremely high temporal resolution. Existing methods encode events into point-cloud-based or voxel-based representations, but suffer from noise and/or information loss. This work proposes the Aligned Event Frame (AET) as a novel event data representation, and a neat framework called Event Frame Net (EFN) The proposed AET and EFN are evaluated on various datasets, and proved to surpass existing state-of-the-art methods by large margins.
• Score: 33.4444564715323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The neuromorphic event cameras, which capture the optical changes of a scene, have drawn increasing attention due to their high speed and low power consumption. However, the event data are noisy, sparse, and nonuniform in the spatial-temporal domain with an extremely high temporal resolution, making it challenging to design backend algorithms for event-based vision. Existing methods encode events into point-cloud-based or voxel-based representations, but suffer from noise and/or information loss. Additionally, there is little research that systematically studies how to handle static and dynamic scenes with one universal design for event-based vision. This work proposes the Aligned Event Tensor (AET) as a novel event data representation, and a neat framework called Event Frame Net (EFN), which enables our model for event-based vision under static and dynamic scenes. The proposed AET and EFN are evaluated on various datasets, and proved to surpass existing state-of-the-art methods by large margins. Our method is also efficient and achieves the fastest inference speed among others.

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