= PDF Reprint, = BibTeX entry, = Online Abstract
R. C. Voorhies, L. Elazary, L. Itti, Neuromorphic Bayesian Surprise for Far-Range Event Detection, In: Proc. 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Beijing, China, Sep 2012. [2012 acceptance rate: 47.2%] (Cited by 2)
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 outperforms a standard foreground/background segmentation approach as well as a standard visual saliency algorithm.
Note: Best student paper award
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Computational Modeling, Computer Vision
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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