融合多变量序列时空信息的事件早期识别方法

doi:10.3778/j.issn.1002-8331.2206-0429

摘要/Abstract

摘要： 针对事件早期识别算法对多维变量序列时空依赖关系挖掘不足的问题，提出了一种时空融合的事件早期识别算法。将多变量时间序列规整为矩阵化数据输入时间卷积网络，通过学习时间序列的时域特征，对时间序列进行预测。将包含已知值和预测值在内的多变量时间序列送入融合时空信息的深度神经网络进行识别。在动作识别、手写字符识别、口语数字识别等多个任务上的实验结果表明，所提算法的性能优于主流的算法。当事件仅发生2/3时，能以平均93.2%的准确率识别多类别事件，在手写字符识别数据集上的早期识别准确率甚至与完整事件被观测到时的识别准确率相当。

关键词: 多变量时间序列, 事件早期识别, 时空信息融合, 序列预测, 深度神经网络

Abstract: Aiming at the problem that existing early event detection algorithms do not fully exploit the spatial-temporal relationships of multivariate time series, an early event detection method based on spatial-temporal fusion is proposed. Time convolutional network（TCN） is adopted for sequence prediction by learning the temporal patterns from the input temporal events described by multivariate time series. Both the original and predicted time series are concatenated along the temporal dimension and fed into a novel multivariate spatial-temporal neural network（MSTNN） for classification. Experimental results for recognizing partial events in multiple scenarios, including action classification, handwritten character recognition, and spoken digit recognition, demonstrate the benefits of the proposed method compared to existing approaches, which achieves an average accuracy of 93.2% when 2/3 of the temporal events have been observed. The performance on the handwritten character recognition dataset is comparable with the case when the complete event is observed.

Key words: multivariate time series, early event detection, spatial-temporal fusion, sequence prediction, deep neural network

刘绪颖, 卢文达, 王剑, 王雪, 王庆. 融合多变量序列时空信息的事件早期识别方法[J]. 计算机工程与应用, 2023, 59(17): 116-122.

LIU Xuying, LU Wenda, WANG Jian, WANG Xue, WANG Qing. Early Event Detection Based on Multivariate Spatial-Temporal Fusion[J]. Computer Engineering and Applications, 2023, 59(17): 116-122.

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