Dynamic vision sensor (DVS) shows significant advantages on low computational latency

low memory usage and high dynamic range by utilizing the event-driven principle to extract features from moving objects. Current research shows that DVS-based neural networks improve object detection speed obviously. However

the sample sets required by such neural networks mainly rely on specific DVS cameras while lacking efficient generation methods for the sample sets. It limits the application and development of those neural networks. According to the principle of DVS

this paper presents a DVS sample set modeling method based on frame images

in which the sample set can be generated by encoding and normalizing the address-event (AE) data after being trigged by dynamic differential comparisons and logical judgments. The experimental results for modeling the MNIST and CIFAR-10 sample sets show that

the sample set modeled by the proposed method is basically matched with the real DVS cameras. Compared with traditional frame image sample sets

this method can significantly reduce the memory usage. The sample set generated by the proposed modeling method has also been verified by training and testing a typical convolutional neural network.