用游戏引擎模拟神经形态视觉数据，解决异常检测数据不足问题。
Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision
- 基于Unity构建3D场景，随机生成物体运动与旋转行为。
- 按中心极限定理统计标注异常物体，可生成任意规模数据集。
- 适合做事件相机下的目标识别、定位与异常检测研究者使用。
动态视觉传感器（DVS）数据获取受限，制约神经形态计算机视觉研究。现有数据集样本或场景有限。为此，我们提出一种新型数据集仿真框架ANTShapes，基于Unity引擎构建抽象可配置的3D场景，内含随机行为物体，涵盖运动、旋转等属性。物体行为采样与异常标签生成遵循中心极限定理，仅需调整少量参数即可生成任意规模数据集，并导出配套标签与帧数据。ANTShapes通过定制化仿真，弥补事件相机视觉研究中数据匮乏的缺陷，支持目标识别、定位及异常检测等任务。
原文摘要 · Abstract (English)
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.
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