Temporal Tensor Local Binary Pattern: A Novel Local Tensor Time Series Descriptor

Abstract

Time series is very ubiquitous in both the industrial environment and real-life. Capturing the time dependency is very useful for time series analysis. Although the one-dimensional local binary pattern (1D-LBP) can analyze the Univariate time series, it cannot effectively handle the multivariate time series (MTS). The UTS can be considered as zero-order tensor time series (TTS), while the MTS can be considered as one- or higher order TTS. Each variable in MTS depends not only on its past values, but also on the other variables. In this article, we propose a temporal tensor LBP (TTLBP) operator to extract the discriminative temporal features from TTS by extending 1D-LBP operation from the scalar-wise to the tensor-wise. The TTLBP is discriminative and can handle the TTS effectively and straightforwardly. To the best of our knowledge, TTLBP is the first LBP variant for TTS analysis. Furthermore, we also propose a stricter uniform TTLBP to improve robustness and to reduce the high dimensionality. We apply the proposed TTLBP in the edge intelligence-assisted video anomaly detection system. Qualitative and quantitative comparisons demonstrate the effectiveness of the proposed TTLBP.