Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision
🤖 AI Summary
Existing neuromorphic datasets are limited in scale and scenario diversity, hindering research on anomaly detection in event-based vision. To address this, this work proposes ANTShapes, a simulation framework built on the Unity engine that enables configurable 3D abstract scenes. For the first time, it leverages the Central Limit Theorem to statistically model object behaviors, facilitating automatic annotation of anomalous events. The framework supports on-demand generation of large-scale, labeled neuromorphic data, substantially alleviating data scarcity and providing an efficient, scalable simulation platform for tasks such as object recognition, localization, and anomaly detection with event cameras.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
neuromorphic vision
anomaly detection
Dynamic Vision Sensors
dataset simulation
event-based computer vision
Innovation
Methods, ideas, or system contributions that make the work stand out.
neuromorphic vision
anomaly detection
dataset simulation
event-based vision
Dynamic Vision Sensor (DVS)
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