When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features

Daniel Deniz, E. Ros, C. Fermüller, Francisco Barranco
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Abstract

Visual event sensors only output data when changes in the scene happen at very high frequency. This allows for smartly compressing the scene and thus, enabling real-time operation. Despite these advantages, works in the literature have struggled to show a niche for these event-driven approaches compared to conventional sensors, especially when focusing on accuracy performance. In this work, we show a case that fully exploits event sensor advantages: for manipulation action recognition, learning events achieves superior accuracy and time performance. The recognition of manipulation actions requires extracting and learning features from the hand pose and trajectory and the interaction with the object. As shown in our work, approaches based on event sensors are the best fit for extracting these dynamic features contrarily to conventional approaches based on full frames, which mostly extract spatial features and need to reconstruct the dynamics from sequences of frames. Finally, we show how using a tracker to extract the features to be learned only around the hand, we obtain an approach that is scene- and almost object-agnostic and achieves good time performance with a very limited impact in accuracy.
神经形态传感器的性能何时优于相机?从动态特征中学习
视觉事件传感器只在场景发生高频变化时输出数据。这允许巧妙地压缩场景,从而实现实时操作。尽管有这些优势,但与传统传感器相比,文献中的工作一直在努力展示这些事件驱动方法的利基,特别是在关注精度性能时。在这项工作中,我们展示了一个充分利用事件传感器优势的案例:对于操作动作识别,学习事件实现了卓越的准确性和时间性能。操作动作的识别需要从手的姿态和轨迹以及与对象的交互中提取和学习特征。正如我们的工作所示,基于事件传感器的方法最适合提取这些动态特征,而传统的基于全帧的方法主要提取空间特征,需要从帧序列中重建动态。最后,我们展示了如何使用跟踪器来提取只在手周围学习的特征,我们获得了一种场景和几乎与对象无关的方法,并且在精度上影响非常有限的情况下实现了良好的时间性能。
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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