基于异步事件流超图网络的运动目标识别方法

doi:10.1007/s12204-024-2699-y

摘要/Abstract

摘要： 与传统相机的采样方式不同，事件相机可以捕捉异步的事件流数据，其中每个事件由编码像素位置、触发事件和亮度的极性变化表示。本文针对传统相机在处理高动态复杂场景时的局限性问题，提出了一种基于异步事件流超图网络的运动目标步态识别新方法。具体来说, 本文先使用事件相机感知运动目标，采集高时间、空间分辨率的异步事件流数据。与堆叠事件帧的处理方法不同，本文结合超图网络模型进行学习，将异步事件数据编码成超图结构，充分挖掘事件数据的高阶相关性，并设计混合卷积的超图神经网络进行训练，在超图注意力机制和残差结构的优化下实现更高效、更准确的运动目标识别。实验结果表明，所提出的超图结构在处理高动态数据时性能更为优越，在公开的运动目标识别（如步态识别）数据集上具有前沿的效果。

关键词: 超图学习, 事件流, 运动目标识别

Abstract: Unlike traditional video cameras, event cameras capture asynchronous event streams in which each event encodes pixel location, triggers’ timestamps, and the polarity of brightness changes. In this paper, we introduce a novel hypergraph-based framework for moving object classification. Specifically, we capture moving objects with an event camera, to perceive and collect asynchronous event streams in a high temporal resolution. Unlike stacked event frames, we encode asynchronous event data into a hypergraph, fully mining the high-order correlation of event data, and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition. The experimental results show that our method has a good performance in moving object classification (e.g., gait identification).

中图分类号:

TP181

. 基于异步事件流超图网络的运动目标识别方法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 952-961.

YU Nannan, WANG Chaoyi, QIAO Yu, WANG Yuxin, ZHENG Chenglin, ZHANG Qiang, YANG Xin. Hypergraph-Based Asynchronous Event Processing for Moving Object Classification[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 952-961.

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