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This paper introduces four novel event-based object classification datasets generated using the ANTShapes simulation tool, addressing the critical need for high-quality datasets in spiking neural network (SNN) research. By benchmarking these datasets against established spiking datasets like N-MNIST and CIFAR10-DVS, the authors validate the effectiveness of ANTShapes in producing reliable data for event-based vision tasks. The findings confirm that these datasets can significantly enhance the development of SNNs for practical applications in security and edge computing environments.
Four new event-based datasets could redefine the landscape of spiking neural network research by providing the high-quality data needed for robust object classification.
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.
