用于基于事件的神经形态目标分类的ANTShapes基准测试数据集
ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification
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中文总结 AI 辅助
本研究针对基于事件的神经形态目标分类，用ANTShapes工具创建四个难度各异的新数据集，结合卷积SNN与现有脉冲数据集开展基准测试，验证了ANTShapes工具的可用性并提供了新实验数据集。
中文摘要 AI 辅助
基于事件的计算机视觉中的目标分类是一项受到大量研究关注的任务，它是安全和应用计算机视觉领域的基础任务，这些领域通常使用同步帧式相机和计算流程运行。这种方法存在多个实际缺陷：设备的尺寸、重量和功耗可能会阻碍其在极端边缘或隐蔽感知环境中的部署；此外，由于需要发送和接收潜在的敏感数据，基于云或其他设备外计算方法存在固有安全问题，且数据传输会引入延迟，还需要与云基础设施保持持续连接才能运行。部署在神经形态设备上的脉冲神经网络（SNN）试图解决传统方法中的若干问题，但高质量视觉数据集的缺乏阻碍了基于事件的目标分类方法的研究。为此，此前已提出ANTShapes仿真工具用于创建和标记基于事件的视觉数据集。本文使用该工具创建了四个不同难度的新数据集，并针对现有常用于基于事件的视觉研究的脉冲数据集（N-MNIST、CIFAR10-DVS、DVSGesture和POKER-DVS）进行了基准测试，采用卷积SNN执行分类任务。本研究同时提供了四个具有丰富细节的数据集供未来实验使用，并验证了ANTShapes数据集仿真工具的输出符合其预期用途。
英文摘要
Object classification in event-based computer vision is a task that is attracting considerable research attention. Event-based object classification is a fundamental task in the fields of security and applied computer vision, which typically use synchronous frame-based cameras and computing pipelines for operation. This approach has several practical flaws. The size, weight and power consumption of the device could prohibit deployment at the extreme edge or in covert sensing environments. Besides this, there are security concerns inherent in cloud-based or other off-device computation approaches due to the requirement of sending and receiving potentially sensitive data. Furthermore, this transmission of data introduces latency and requires consistent connectivity to the cloud infrastructure to function. The use of Spiking Neural Networks (SNNs) hosted on neuromorphic devices attempts to solve several issues present in this conventional approach. Research into event-based object classification methods are hindered by the lack of high-quality vision datasets to use. To this end, the ANTShapes simulation tool has been previously proposed to create and label event-based vision datasets. In this paper, four novel datasets of varying difficulties are created using the tool and are benchmarked against existing spiking datasets commonly used for event-based vision research (N-MNIST, CIFAR10-DVS, DVSGesture and POKER-DVS). Classification is performed using a convolutional SNN. This work simultaneously provides four datasets with rich details for future experiments to use and validates the output of the ANTShapes dataset simulation tool as being suitable for its purpose.
发表机构
- University of York（约克大学）
- Cardiff University（卡迪夫大学）
- University of Manchester（曼彻斯特大学）
- University of Stirling（斯特灵大学）
- University of Southampton（南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。
