Detecting actions by integrating sequential symbolic and sub-symbolic information in human activity recognition

Authors:
Michael Glodek;Friedhelm Schwenker;Günther Palm
Affiliations:
Institute of Neural Information Processing, Ulm University, Ulm, Germany;Institute of Neural Information Processing, Ulm University, Ulm, Germany;Institute of Neural Information Processing, Ulm University, Ulm, Germany
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

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Abstract

Detecting human activities is a challenging field for sequential algorithms in machine learning and several approaches have already been proposed. One approach is to make use of the hierarchical structure of the activities to be classified by subdividing them into more elementary actions [12]. Alternatively the fusing of additional context information has been investigated to obtain a more meaningful feature space [10]. Within this work both approaches are pursued by utilizing the layered architecture proposed by Oliver et al. [13] with the conditioned hidden Markov model (CHMM) [8]. The model is evaluated using a dataset containing sequential sub-symbolic information (i.e. the position of body parts) and symbolic information (i.e. the detected object the person interacts with). The results outperform the classical approach making no use of the additional symbolic information.