Abstract
The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classification algorithm. Here we address recognition of human actions using the reservoir computing algorithm, which allows us to focus on the classifier stage. We introduce a new training method for the reservoir computer, based on "Timesteps Of Interest", which combines in a simple way short and long time scales. We study the performance of this algorithm using both numerical simulations and a photonic implementation based on a single non-linear node and a delay line on the well known KTH dataset. We solve the task with high accuracy and speed, to the point of allowing for processing multiple video streams in real time. The present work is thus an important step towards developing efficient dedicated hardware for video processing.
Abstract (translated)
视频对人类行为识别的研究是计算机视觉领域最为活跃的研究领域之一。标准的研究方法包括对原始视频数据的较为复杂的预处理阶段,随后是相对简单的分类算法。在这里,我们使用 Reservoir Computing算法来解决人类行为识别问题,从而使我们能够专注于分类器阶段。我们提出了一种新的 Reservoir Computer 的训练方法,基于“感兴趣的时间步”,它以一种简单的方式将短期和长期时间尺度组合在一起。我们使用数值模拟和基于单个非线性节点和延迟线的光子实现来研究该算法的性能。我们以一种高精度和高速度的方式解决了任务,以至于能够实时处理多个视频流。因此,当前工作是开发高效专门用于视频处理的硬件的重要步骤。
URL
https://arxiv.org/abs/2305.15283