Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2002.05809v2
• Date: Thu, 9 Sep 2021 21:34:45 GMT
• Title: Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition
• Authors: Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis
• Abstract summary: This paper revisits conventional sequential modeling approaches, aiming to address the problem of capturing time-varying temporal dependency patterns. We propose a different formulation of HMMs, whereby the dependence on past frames is dynamically inferred from the data. We derive a tractable inference algorithm based on the forward-backward algorithm.
• Score: 7.9603223299524535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hidden Markov Models (HMMs) comprise a powerful generative approach for modeling sequential data and time-series in general. However, the commonly employed assumption of the dependence of the current time frame to a single or multiple immediately preceding frames is unrealistic; more complicated dynamics potentially exist in real world scenarios. This paper revisits conventional sequential modeling approaches, aiming to address the problem of capturing time-varying temporal dependency patterns. To this end, we propose a different formulation of HMMs, whereby the dependence on past frames is dynamically inferred from the data. Specifically, we introduce a hierarchical extension by postulating an additional latent variable layer; therein, the (time-varying) temporal dependence patterns are treated as latent variables over which inference is performed. We leverage solid arguments from the Variational Bayes framework and derive a tractable inference algorithm based on the forward-backward algorithm. As we experimentally show, our approach can model highly complex sequential data and can effectively handle data with missing values.

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