【摘要】 动态视觉传感器(Dynamic Vision Sensor,DVS)利用事件驱动原理实现运动目标的快速提取,具有低延迟、低存储空间和高动态范围等优势.目前研究表明,基于DVS的神经网络在目标检测等领域具有明显的速度优势.但是,这类神经网络在训练时所需要的样本集主要依赖DVS相机产生,缺少高效的样本集生成方法,这制约了这类神经网络的应用与发展.本文根据DVS原理,提出了一种基于帧图像的DVS建模以及样本集建模方法.该方法设定每个像素单元独立工作,经过动态差分和逻辑判断后输出触发的地址-事件数据,这些数据通过编码和归一化生成神经网络训练时所需要的样本集.通过对MNIST和CIFAR-10样本集建模的实验结果表明,该建模方法效果与DVS相机基本一致;与基于帧图像的存储方式相比,该样本集可以明显减少存储空间.该方法所生成样本集已经通过卷积神经网络训练和测试验证.更多还原

【Abstract】 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.更多还原