E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition

• URL: http://arxiv.org/abs/2112.03596v1
• Date: Tue, 7 Dec 2021 09:43:08 GMT
• Title: E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition
• Authors: Chiara Plizzari, Mirco Planamente, Gabriele Goletto, Marco Cannici, Emanuele Gusso, Matteo Matteucci, Barbara Caputo
• Abstract summary: Event cameras capture pixel-level intensity changes in the form of "events" N-EPIC-Kitchens is the first event-based camera extension of the large-scale EPIC-Kitchens dataset. We show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time.
• Score: 21.199869051111367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based cameras. These characteristics make them a perfect fit to several real-world applications such as egocentric action recognition on wearable devices, where fast camera motion and limited power challenge traditional vision sensors. However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications. In this paper, we show that event data is a very valuable modality for egocentric action recognition. To do so, we introduce N-EPIC-Kitchens, the first event-based camera extension of the large-scale EPIC-Kitchens dataset. In this context, we propose two strategies: (i) directly processing event-camera data with traditional video-processing architectures (E$^2$(GO)) and (ii) using event-data to distill optical flow information (E$^2$(GO)MO). On our proposed benchmark, we show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time, and an improved performance of up to 4% with respect to RGB only information.

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