Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision
This work provides a tool for researchers in event-based computer vision to generate custom neuromorphic datasets, mitigating the current limitations in data availability for applications like object recognition, localization, and anomaly detection.
The authors developed ANTShapes, a novel dataset simulation framework built in Unity, to address the scarcity of neuromorphic vision datasets. This framework can generate datasets with an arbitrary number of samples, labels, and frame data, simulating abstract 3D scenes with objects exhibiting random motion and rotation, with anomalous objects statistically labeled.
Limitations on the availability of Dynamic Vision Sensors (DVS) present a fundamental challenge to researchers of neuromorphic computer vision applications. In response, datasets have been created by the research community, but often contain a limited number of samples or scenarios. To address the lack of a comprehensive simulator of neuromorphic vision datasets, we introduce the Anomalous Neuromorphic Tool for Shapes (ANTShapes), a novel dataset simulation framework. Built in the Unity engine, ANTShapes simulates abstract, configurable 3D scenes populated by objects displaying randomly-generated behaviours describing attributes such as motion and rotation. The sampling of object behaviours, and the labelling of anomalously-acting objects, is a statistical process following central limit theorem principles. Datasets containing an arbitrary number of samples can be created and exported from ANTShapes, along with accompanying label and frame data, through the adjustment of a limited number of parameters within the software. ANTShapes addresses the limitations of data availability to researchers of event-based computer vision by allowing for the simulation of bespoke datasets to suit purposes including object recognition and localisation alongside anomaly detection.