[2602.23514] Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.23514 (cs) [Submitted on 26 Feb 2026] Title:Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision Authors:Mike Middleton, Teymoor Ali, Hakan Kayan, Basabdatta Sen Bhattacharya, Charith Perera, Oliver Rhodes, Elena Gheorghiu, Mark Vousden, Martin A. Trefzer View a PDF of the paper titled Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision, by Mike Middleton and 8 other authors View PDF HTML (experimental) Abstract: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 accompany...