Investigating Event-Based Cameras for Video Frame Interpolation in Sports

TL;DR

This paper explores the use of event-based cameras combined with deep learning for generating slow-motion sports videos, demonstrating practical utility and laying groundwork for future research.

Contribution

It introduces a novel bi-camera setup with RGB and event-based cameras for sports video interpolation and evaluates the effectiveness of an existing VFI model in this context.

Findings

TimeLens effectively generates sports slow-motion videos

Event-based cameras provide valuable motion information

The approach enhances slow-motion video quality in sports

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.