Time-Ordered Recent Event (TORE) Volumes for Event Cameras
R. Wes Baldwin, Ruixu Liu, Mohammed Almatrafi, Vijayan Asari, Keigo, Hirakawa

TL;DR
The paper introduces TORE volumes, a novel event representation for event cameras that efficiently preserves raw timing information, improving performance across various tasks without relying on interpolated data.
Contribution
It presents TORE volumes, a new bio-inspired, memory-efficient event representation that enhances machine learning performance on event camera data.
Findings
Significantly improves state-of-the-art results in event denoising.
Enhances image reconstruction and classification accuracy.
Facilitates human pose estimation with higher precision.
Abstract
Event cameras are an exciting, new sensor modality enabling high-speed imaging with extremely low-latency and wide dynamic range. Unfortunately, most machine learning architectures are not designed to directly handle sparse data, like that generated from event cameras. Many state-of-the-art algorithms for event cameras rely on interpolated event representations - obscuring crucial timing information, increasing the data volume, and limiting overall network performance. This paper details an event representation called Time-Ordered Recent Event (TORE) volumes. TORE volumes are designed to compactly store raw spike timing information with minimal information loss. This bio-inspired design is memory efficient, computationally fast, avoids time-blocking (i.e. fixed and predefined frame rates), and contains "local memory" from past data. The design is evaluated on a wide range of challenging…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural Networks and Applications
