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To help meet the increasing need for dynamic vision sensor (DVS) event cameradata, this paper proposes the v2e toolbox that generates realistic syntheticDVS events from intensity frames. It also clarifies incorrect claims about DVSmotion blur and latency characteristics in recent literature. Unlike othertoolboxes, v2e includes pixel-level Gaussian event threshold mismatch, finiteintensity-dependent bandwidth, and intensity-dependent noise. Realistic DVSevents are useful in training networks for uncontrolled lighting conditions.The use of v2e synthetic events is demonstrated in two experiments. The firstexperiment is object recognition with N-Caltech 101 dataset. Results show thatpretraining on various v2e lighting conditions improves generalization whentransferred on real DVS data for a ResNet model. The second experiment showsthat for night driving, a car detector trained with v2e events shows an averageaccuracy improvement of 40% compared to the YOLOv3 trained on intensity frames.

v2e: From Video Frames to Realistic DVS Events