# Video to Events: Recycling Video Datasets for Event Cameras Source: [Video to Events: Recycling Video Datasets for Event Cameras]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:11:14.110Z Authors: Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carri\\'o, Davide Scaramuzza Published: 2019-12-06T13:10:59.000Z ## Abstract Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high dynamic range (HDR), high temporal resolution, and no motion blur. Recently, novel learning approaches operating on event data have achieved impressive results. Yet, these methods require a large amount of event data for training, which is hardly available due the novelty of event sensors in computer vision research. In this paper, we present a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to synthetic event data. This unlocks the use of a virtually unlimited number of existing video datasets for training networks designed for real event data. We evaluate our method on two relevant vision tasks, i.e., object recognition and semantic segmentation, and show that models trained on synthetic events have several benefits: (i) they generalize well to real event data, even in scenarios where standard-camera images are blurry or overexposed, by inheriting the outstanding properties of event cameras; (ii) they can be used for fine-tuning on real data to improve over state-of-the-art for both classification and semantic segmentation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Event-based Semantic Segmentation on DDD17 (test)](): Event-based Semantic Segmentation; DDD17 (test); mIoU (General): 56.01. - [Object Classification on N-Caltech101 (test)](): Object Classification; N-Caltech101 (test); Accuracy: 80.7. - [Semantic Segmentation on DDD17 (test)](): Semantic segmentation; DDD17 (test); mIoU: 56.01. ## Original sources and implementation [Original paper]() [PDF]()