Investigating Event-Based Cameras for Video Frame Interpolation in Sports
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917711459647488 |
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| author | Deckyvere, Antoine Cioppa, Anthony Giancola, Silvio Ghanem, Bernard Van Droogenbroeck, Marc |
| author_facet | Deckyvere, Antoine Cioppa, Anthony Giancola, Silvio Ghanem, Bernard Van Droogenbroeck, Marc |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating Event-Based Cameras for Video Frame Interpolation in Sports Deckyvere, Antoine Cioppa, Anthony Giancola, Silvio Ghanem, Bernard Van Droogenbroeck, Marc Computer Vision and Pattern Recognition 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. |
| title | Investigating Event-Based Cameras for Video Frame Interpolation in Sports |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.02370 |