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Journal Articles IEEE Transactions on Intelligent Transportation Systems Year : 2022

Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras

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 formulate a novel dense representation for the sparse events flow, in the form of the "inverse exponential distance surface". It serves as an interim frame, designed for the use of proven, state-of-the-art frame-based optical flow computation methods. We evaluate our approach on both low-and high-resolution driving sequences, and show that it often achieves better results than the current state of the art, while also reaching higher frame rates, 250Hz at 346×260 pixels and 77Hz at 1280×720 pixels.
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Dates and versions

hal-03476956 , version 1 (13-12-2021)

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Vincent Brebion, Julien Moreau, Franck Davoine. Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (9), pp.15066-15078. ⟨10.1109/TITS.2021.3136358⟩. ⟨hal-03476956⟩
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