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

TL;DR

This paper introduces ANTShapes, a Unity-based simulation framework for generating customizable neuromorphic vision datasets to facilitate anomaly detection and other computer vision tasks, addressing data scarcity issues.

Contribution

The paper presents a novel, configurable dataset simulator for neuromorphic vision, enabling the creation of diverse, labeled datasets for anomaly detection and related applications.

Findings

ANTShapes can generate unlimited samples with customizable behaviors.

The framework supports anomaly labeling and scene configuration.

It addresses data scarcity in neuromorphic vision research.

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 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.