Abstract
Hidden Markov models are popular tools for gesture recognition. Once the generative processes of gestures have been identified, an observation sequence is usually classified as the gesture having the highest likelihood, thus ignoring possible prior information. In this paper, we consider two potential improvements of such methods: the inclusion of prior information, and the possibility of considering convex sets of probabilities (in the likelihoods and the prior) to infer imprecise, but more reliable, predictions when information is insufficient. We apply the proposed approach to technical gestures, typically characterized by severe class imbalance. By modelling such imbalances as a prior information, we achieve more accurate results, while the imprecise quantification is shown to produce more reliable estimates.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antonucci A, de Rosa R, Giusti A, Cuzzolin F (2015) Robust classification of multivariate time series by imprecise hidden Markov models. Int J Approx Reason 56(B):249–263
Bevilacqua F, Zamborlin B, Sypniewski A, Schnell N, Guédy F, Rasamimanana N (2010) Continuous Realtime Gesture Following and Recognition. In: Gesture in embodied communication and human-computer interaction: 8th international gesture workshop, GW 2009, Revised Selected Papers. Springer, pp 73–84
Bouchard G, Triggs B (2004) The tradeoff between generative and discriminative classifiers. In: International symposium on computational statistics, pp 721–728
Chaudhary A, Raheja JL, Das K, Raheja S (2011) Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey. Int J Comput Sci Eng Surv 2(1):122–133
Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247–2253
Liu K, Chen C, Jafari R, Kehtarnavaz N (2014) Multi-HMM classification for hand gesture recognition using two differing modality sensors. In: Circuits and systems conference (DCAS). IEEE, pp 1–4
Mauá DD, Antonucci A, de Campos CP (2015) Hidden Markov models with set-valued parameters. Neurocomputing 180:94–107
Neverova N, Wolf C, Taylor GW, Nebout F (2014) Multi-scale deep learning for gesture detection and localization. In: ECCV workshop on looking at people
Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
Soullard Y, Saveski M, Artières T (2014) Joint semi-supervised learning of hidden conditional random fields and hidden Markov models. Pattern Recogn Lett 37:161–171
Walley P (1996) Inferences from multinomial data: learning about a bag of marbles. J R Stat Soc B 58(1):3–57
Zaffalon M, Corani G, Mauá DD (2012) Evaluating credal classifiers by utility-discounted predictive accuracy. Int J Approx Reason 53(8):1282–1301
Acknowledgments
This work is founded by the European Union and the French region Picardie. Europe acts in Picardie with the European Regional Development Fund (ERDF). This work is supported by the ANR UML-net project, grant ANR-14-CE24-0026 of the French Agence Nationale de la Recherche (ANR).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Soullard, Y., Antonucci, A., Destercke, S. (2017). Technical Gestures Recognition by Set-Valued Hidden Markov Models with Prior Knowledge. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-42972-4_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42971-7
Online ISBN: 978-3-319-42972-4
eBook Packages: EngineeringEngineering (R0)