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arXiv · 2407.02370

Investigating Event-Based Cameras for Video Frame Interpolation in Sports

Antoine Deckyvere·Anthony Cioppa·Silvio Giancola·Bernard Ghanem·Marc Van Droogenbroeck
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Abstract

Slow-motion replays provide a thrilling perspective on pivotal moments within sports games, offering a fresh and captivating visual experience. However, capturing slow-motion footage typically demands high-tech, expensive cameras and infrastructures. Deep learning Video Frame Interpolation (VFI) techniques have emerged as a promising avenue, capable of generating high-speed footage from regular camera feeds. Moreover, the utilization of event-based cameras has recently gathered attention as they provide valuable motion information between frames, further enhancing the VFI performances. In this work, we present a first investigation of event-based VFI models for generating sports slow-motion videos. Particularly, we design and implement a bi-camera recording setup, including an RGB and an event-based camera to capture sports videos, to temporally align and spatially register both cameras. Our experimental validation demonstrates that TimeLens, an off-the-shelf event-based VFI model, can effectively generate slow-motion footage for sports videos. This first investigation underscores the practical utility of event-based cameras in producing sports slow-motion content and lays the groundwork for future research endeavors in this domain.

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Antoine Deckyvere, Anthony Cioppa, Silvio Giancola, Bernard Ghanem, Marc Van Droogenbroeck. 2024-07-03. Investigating Event-Based Cameras for Video Frame Interpolation in Sports. https://arxiv.org/abs/2407.02370

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