Complexity-based motion features and their applications to action recognition by hierarchical spatio-temporal naïve Bayes classifier

In this paper, we propose a complexity-based motion feature learning method and hierarchical spatio-temporal naïve Bayes classifier for human action recognition. As a motion feature learning method, we developed a complexity-based subsequence of time series clustering (C-STSC) method to learn time series codewords from a human motion trajectory. The key to the C-STSC method is to measure the importance of each subsequence in time series data through to use of a complexity measure. Next, time series codewords are learned on the basis of the important subsequences by using a clustering algorithm. Moreover, we also propose a hierarchical spatio-temporal naïve Bayes classifier (HST-NBC) to classify the C-STSC features, where both the codeword-type and its spatio-temporal information is explicitly represented as a composite node in a Bayesian network framework. To validate the proposed method, we present experimental results of the proposed approach with respect to several open datasets.