Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras
Vincent Brebion, Julien Moreau, Franck Davoine

TL;DR
This paper presents a real-time optical flow framework for event cameras of varying resolutions, using a novel dense representation to improve accuracy and speed in vehicular perception tasks under challenging conditions.
Contribution
It introduces the inverse exponential distance surface for dense event flow representation, enabling real-time optical flow computation on high-resolution neuromorphic sensors.
Findings
Achieves higher frame rates up to 250Hz at low resolution and 77Hz at high resolution.
Often outperforms existing state-of-the-art methods in accuracy.
Effective for high-speed vehicular perception under complex lighting.
Abstract
Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional framebased sensors show their limits with blur and over- or underexposed pixels. Thanks to these unique properties, they represent nowadays an highly attractive sensor for ITS-related applications. Event-based optical flow (EBOF) has been studied following the rise in popularity of these neuromorphic cameras. The recent arrival of high-definition neuromorphic sensors, however, challenges the existing approaches, because of the increased resolution of the events pixel array and a much higher throughput. As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras. We…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural dynamics and brain function · Neuroscience and Neural Engineering
