Specialized visual sensor coupled to a dynamic neural field for embedded attentional process

Marino Rasamuel, Lyes Khacef, Laurent Rodriguez, Benoît Miramond
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引用次数: 1

Abstract

Machine learning has recently taken the leading role in machine vision through deep learning algorithms. It has brought the best results in object detection, recognition and tracking. Nevertheless, these systems are computationally expensive since they need to process the whole images from the camera for producing such results. Consequently, they require important hardware resources that limit their use for embedded applications. In the other hand, we find a more efficient mechanism in biological systems. The brain, indeed, enables an attentional process to focus on the relevant information from the environment, and hence process only a sub-part of the visual field at a time. In this work, we implement a brain-inspired attentional process through dynamic neural fields that is integrated in two types of specialized visual sensors: frame-based and event-based cameras. We compare the obtained results on tracking performances and power consumption in the context of embedded recognition and tracking.
专用视觉传感器与动态神经场耦合,用于嵌入式注意过程
最近,机器学习通过深度学习算法在机器视觉领域占据了主导地位。在目标检测、识别和跟踪方面取得了较好的效果。然而,这些系统的计算成本很高,因为它们需要处理来自相机的整个图像才能产生这样的结果。因此,它们需要重要的硬件资源,这限制了它们在嵌入式应用程序中的使用。另一方面,我们在生物系统中发现了一种更有效的机制。的确,大脑使注意力过程能够集中于来自环境的相关信息,因此一次只处理视野的一小部分。在这项工作中,我们通过动态神经场实现了一个由大脑启发的注意力过程,该过程集成在两种类型的专业视觉传感器中:基于帧的和基于事件的相机。我们比较了在嵌入式识别和跟踪环境下获得的跟踪性能和功耗结果。
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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