Neuromorphic Bayesian Surprise for Far-Range Event Detection

Randolph Voorhies, Lior Elazary, L. Itti
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引用次数: 4

Abstract

In this paper we address the problem of detecting small, rare events in very high resolution, far-field video streams. Rather than learning color distributions for individual pixels, our method utilizes a uniquely structured network of Bayesian learning units which compute a combined measure of "surprise" across multiple spatial and temporal scales on various visual features. The features used, as well as the learning rules for these units are derived from recent work in computational neuroscience. We test the system extensively on both real and virtual data, and show that it out-performs a standard foreground/background segmentation approach as well as a standard visual saliency algorithm.
远距离事件检测的神经形态贝叶斯惊喜
在本文中,我们解决了在非常高分辨率的远场视频流中检测小的、罕见的事件的问题。我们的方法不是学习单个像素的颜色分布,而是利用贝叶斯学习单元的独特结构网络,在各种视觉特征的多个空间和时间尺度上计算“惊喜”的组合度量。所使用的特征,以及这些单元的学习规则都来源于最近的计算神经科学工作。我们在真实和虚拟数据上对系统进行了广泛的测试,并表明它优于标准的前景/背景分割方法以及标准的视觉显著性算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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