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

2026-03-02 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Computer Vision · Depth: Advanced, long

Summary

The Anomalous Neuromorphic Tool for Shapes (ANTShapes) is a novel dataset simulation framework developed in the Unity engine to address the scarcity of neuromorphic vision datasets for anomaly detection. This framework simulates abstract, configurable 3D scenes with objects exhibiting randomly generated behaviors like motion and rotation. ANTShapes uses a statistical process, based on central limit theorem principles, to sample object behaviors and label anomalous actions. It can generate datasets with an arbitrary number of samples, along with label and frame data, by adjusting a few parameters. The tool supports the simulation of bespoke datasets for applications such as object recognition, localization, and anomaly detection, specifically for event-based computer vision research.

Key takeaway

For research scientists developing Spiking Neural Networks (SNNs) for anomaly detection, ANTShapes offers a critical solution to the limited availability of neuromorphic vision datasets. You should consider using this Unity-based simulation framework to generate highly customizable, abstract datasets, allowing for controlled experimentation with SNN learning characteristics before moving to more realistic scenarios. This tool enables focused evaluation of anomaly detection models by precisely defining and labeling anomalous behaviors.

Key insights

ANTShapes simulates neuromorphic vision datasets for anomaly detection, overcoming DVS data scarcity via configurable 3D scenes.

Principles

• Anomaly definition uses statistical deviation from expected behaviors.
• Central limit theorem guides behavior distribution and anomaly labeling.

Method

ANTShapes, built in Unity, simulates abstract 3D scenes with configurable object behaviors and statistical anomaly labeling. It generates event data by comparing pixel intensity changes between simulated frames.

In practice

• Generate custom neuromorphic datasets for SNN training.
• Define anomalies based on object motion, rotation, and other attributes.
• Export datasets with labels and frame data for research.

Topics

• Neuromorphic Datasets
• Anomaly Detection
• Event-based Computer Vision
• Dataset Simulation
• Spiking Neural Networks

Code references

• EDGYOrganism/ANTShapes

Best for: Research Scientist, AI Researcher, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.