Robust incremental hidden conditional random fields for human action recognition

Michalis Vrigkas, Ermioni Mastora, Christophoros Nikou, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Hidden conditional random fields (HCRFs) are a powerful supervised classification system, which is able to capture the intrinsic motion patterns of a human action. However, finding the optimal number of hidden states remains a severe limitation for this model. This paper addresses this limitation by proposing a new model, called robust incremental hidden conditional random field (RI-HCRF). A hidden Markov model (HMM) is created for each observation paired with an action label and its parameters are defined by the potentials of the original HCRF graph. Starting from an initial number of hidden states and increasing their number incrementally, the Viterbi path is computed for each HMM. The method seeks for a sequence of hidden states, where each variable participates in a maximum number of optimal paths. Thereby, variables with low participation in optimal paths are rejected. In addition, a robust mixture of Student’s t-distributions is imposed as a regularizer to the parameters of the model. The experimental results on human action recognition show that RI-HCRF successfully estimates the number of hidden states and outperforms all state-of-the-art models.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings
EditorsKai Xu, Stephen Lin, Richard Boyle, Bilal Alsallakh, Matt Turek, Srikumar Ramalingam, George Bebis, Bahram Parvin, Jing Yang, Jonathan Ventura, Darko Koracin, Eduardo Cuervo
Number of pages11
ISBN (Print)9783030038007
StatePublished - 2018
Event13th International Symposium on Visual Computing, ISVC 2018 - Las Vegas, NV, United States
Duration: Nov 19 2018Nov 21 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11241 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Symposium on Visual Computing, ISVC 2018
Country/TerritoryUnited States
CityLas Vegas, NV


  • Hidden markov model action recognition
  • Student’s t-distribution hidden conditional random fields

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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